Jobs ML / AI

Responsibilities:

• Own and support core ML infrastructure (MLflow, CVAT, GPU/CPU cloud instances, internal data tools).
• Assist Data Engineers with deploying and maintaining data pipelines.
• Act as the primary contact for ML and Data teams on infra-related requests.
• Improve reliability and efficiency by adding automation, monitoring, and tooling enhancements.
   

Must have:

• Knowledge of Python, Terraform, Docker, Linux.
• Hands-on experience with cloud environments (Azure prefered).
• Understanding of the end-to-end ML lifecycle (data → training → evaluation → deployment → monitoring).

Nice to have:

• Familiarity with Kubernetes.
• Experience with FFmpeg or GStreamer for video processing and streaming.
• Experience with model serving frameworks and inference formats (ONNX, TensorRT, OpenVINO, NVIDIA Triton).
• Experience deploying computer vision models, especially to edge/embedded devices (NVIDIA Jetson, Intel platforms, Raspberry Pi).

• Надаємо бронювання від мобілізації

ORIL is seeking a highly skilled Senior AI/ML Engineer to architect and scale our mission-critical, AI-driven data platform. At the core of our engineering culture is a steadfast commitment to performance, reliability, and uncompromising data integrity. The ideal candidate is an autonomous problem-solver who thrives in an agile environment, takes end-to-end ownership of complex systems, and pairs a bias for action with a dedication to robust, stable deployments.

 

Project:

Our client is a US-based innovator providing a centralized data platform for multi-family real estate. The product uses BI and AI analytics to help operators optimize marketing spend and operational efficiency. A core AI feature of this platform is an intelligent chatbot designed to help users seamlessly manage workflows, query data, and surface actionable insights.

What are we looking for?

• 5+ years of experience building and owning production-grade backend and ML systems in Python;
• Proven experience designing and deploying complex, multi-step LLM workflows and conversational agents (chatbots) using frameworks like LangChain or LangGraph;
• Deep understanding of RAG (Retrieval-Augmented Generation) pipelines: from chunking and embedding strategies to retrieval optimization and prompt engineering;
• Experience with "Text-to-SQL" or translating natural language into complex data queries, as the chatbot will interact heavily with BI analytics;
• Experience with LLM observability, tracing, and evaluation frameworks (e.g., Langfuse, Braintrust, Arize Phoenix, Ragas, or DeepEval);
• Experience with systematic prompt lifecycle management, versioning, and algorithmic optimization (e.g., DSPy);
• Experience profiling and minimizing time-to-first-token (TTFT) and overall response latency for real-time conversational agents;
• Strong analytical skills with the ability to explore datasets, identify patterns, measure outcomes, and derive actionable insights; 
• Familiar with data cleaning and normalization techniques to ensure accurate, consistent, and reliable data for analysis and AI-driven applications;
• Curious about how multi-family real estate works and motivated to understand the key metrics behind the business (marketing spend, operational efficiency etc.) to better tune AI outputs;
• English level: Upper-Intermediate (B2+).

What will you do?

• Design, develop, and own the core AI architecture powering the platform's intelligent chatbot and automated BI insights;
• Build and refine advanced RAG pipelines to ensure the chatbot surfaces accurate;
• Develop robust AI agents capable of managing platform processes and executing complex operational tasks on behalf of the user;
• Set up and maintain automated CI/CD gates that run LLM evals and integration tests;
• Maintain a high shipping tempo, continuously iterating on chatbot features based on user feedback and internal telemetry;
• Diagnose and fix complex issues in the AI pipeline using advanced tracing and observability tools;
• Work closely with data engineers and product managers to refine the technical roadmap and ensure the AI seamlessly integrates with the broader BI platform.

We Offer:

Working conditions:

• Ability to grow with an ambitious niche-focused company
• Competitive compensation;
• Accounting Support Services;
• Flexibility to work from anywhere (fully remotely or in our office in Lviv);
• Comprehensive day-off policy, including public holidays and special events
• Education & certification budget + corporate education events and trainings
• Wellness budget
• The environment and culture that promotes personal and team development.

Application Process:

• Initial Screening: Call with Recruiter.
• Technical Interview.
• Client Interview.
   

Ready to build the future of AI-driven real estate operations? Apply now and let’s get to work!

Ми шукаємо AI Engineer, який підсилить нашу команду розробки штучного інтелекту. Це роль не про "просте підключення APІ чи поверхеневий інжиніринг запитів", а про глибоку інженерну й системну рооту на інтеграцію LLM, розробкою аріхектурних даних.
Якщо ти зацікавлений у даній позиції, читай нижче опис по вимогам/задачам, та якщо ти відповідаєш вимогам, залишай резюме!

Вимоги до досвіду:

• досвід комерційної розробки: не менше 3 років;
  Інструменти та технології: Python, REST API, OpenAPI/Swagger, JSON, Docker, PostgreSQL/MongoDB, Redis, CI/CD;
   
• впевнене володіння Python як основною мовою розробки;
  Необхідна екосистема Python: Python 3.10+, FastAPI, Flask (як плюс), Pydantic, Pydantic AI, asyncio, aiohttp/httpx, requests, SQLAlchemy, Alembic, Celery/RQ, pytest, unittest, mypy, ruff, black, isort, pre-commit, logging, structlog, pathlib, typing, dataclasses;
   
• досвід розробки backend-сервісів, API та інтеграцій;
  Інструменти та технології: FastAPI, REST API, WebSockets, SSE, OpenAPI/Swagger, Postman, JSON Schema, PostgreSQL, Redis, SQLAlchemy, Docker, docker-compose, GitHub Actions/GitLab CI;
   
• обов'язкове вміння самостійно писати чистий, підтримуваний та архітектурно коректний код без використання додаткових ШІ-інструментів (AI tools);
  Необхідні знання: OOP (ООП), SOLID, DRY, KISS, YAGNI, dependency injection, clean architecture, layered architecture, design patterns, error handling, logging, unit/integration testing, code review;
   
• досвід роботи з Docker та базовою інфраструктурою розгортання;
  Інструменти та технології: Docker, Docker Compose, Dockerfile, volumes, networks, multi-stage builds, environment variables, secrets, logs;
   
• досвід розробки ШІ-продуктів (AI products) на базі пропрієтарних моделей;
  Інструменти та технології: OpenAI API, Anthropic Claude API, Google Gemini API, OpenRouter, LiteLLM;
   
• досвід розробки ШІ-продуктів на базі local-hosted моделей;
  Інструменти та технології: vLLM, Ollama, llama.cpp, Text Generation Inference, Hugging Face Transformers, Hugging Face Hub, sentence-transformers, PyTorch, tokenizers, GGUF, AWQ, GPTQ, quantization, batching, streaming inference;
   

• досвід роботи з vLLM / llama-серверами та налаштуванням параметрів інференсу;
  Необхідні знання: context window, max tokens, temperature, top_p, top_k, repetition penalty, stop sequences, seed, batch size, concurrency, latency, throughput, tokens/sec, GPU memory, KV cache, cost per request;

  Інструменти: vLLM, Ollama, llama.cpp, TGI, Prometheus/Grafana (як плюс);
   

• досвід підбору моделей під робочі завдання;
  Необхідні знання: порівняння моделей за якістю, вартістю, latency, context window, multilingual quality, tool calling, structured output, safety, hallucination rate;
  Інструменти: OpenRouter, LiteLLM, Hugging Face Leaderboards, LMSYS/Chatbot Arena (як орієнтир), Langfuse, LangSmith, PromptFoo, DeepEval, Ragas;
   
• досвід роботи з vector RAG;
  Інструменти та технології: LangChain, LangGraph, LlamaIndex, Haystack, Qdrant, Weaviate, Pinecone, Chroma, FAISS, pgvector, Elasticsearch/OpenSearch, BM25, hybrid search, re-ranking;
  Необхідні знання: chunking strategy, embeddings, metadata filtering, query rewriting, retrieval evaluation, rerankers, grounding, citations, hallucination control;
   
• досвід роботи з graph RAG / knowledge graph RAG;
  Інструменти та технології: Neo4j, NetworkX, GraphRAG, LangChain Graph QA, LlamaIndex Knowledge Graph, RDF/SPARQL (як плюс);
  Необхідні знання: entity extraction, relationship extraction, graph traversal, semantic links, hybrid retrieval, context assembly;
   
• досвід побудови агентних систем;
  Інструменти та технології: LangGraph, LangChain Agents, LlamaIndex Agents, CrewAI, AutoGen, Semantic Kernel, Pydantic AI, OpenAI function calling / tool calling, Anthropic tool use, MCP;
  Необхідні знання: ReAct, planner-executor, supervisor pattern, multi-agent orchestration, memory, tool routing, state management, fallback logic, human-in-the-loop, guardrails;
   
• досвід роботи з AI workflow engine, обов'язково n8n;
  Інструменти та технології: n8n, webhook nodes, HTTP Request node, AI Agent node, credentials, triggers, queues, error workflows, custom nodes, workflow versioning;
   
• досвід побудови evaluation та observability для LLM-продуктів;
  Інструменти та технології: Langfuse, LangSmith, PromptFoo, Ragas, DeepEval, TruLens, Arize Phoenix, Weights & Biases Weave, Helicone, OpenTelemetry;
  Метрики: accuracy, groundedness, relevance, faithfulness, hallucination rate, refusal rate, latency, cost per request, token usage, context utilization, regression rate;
   
• розуміння інформаційної безпеки при роботі з ШІ;
  Необхідні знання: PII, sensitive data, prompt injection, data leakage, secrets management, RBAC, audit logs, sandboxing tools, vendor data policies;
   
• англійська мова: Не нижче Upper-Intermediate. Необхідна для читання документації, роботи з API, аналізу моделей, технічного листування та підготовки design docs.
   

Буде плюсом:

• знання Node.js;
• глибокі знання context engineering;
• глибокі знання NLP;
• досвід fine-tuning моделей.

Основні задачі:

• розробляти production-ready ШІ-сервіси, backend-компоненти, API та інтеграції для внутрішніх і зовнішніх ШІ-продуктів компанії;
• писати чистий, підтримуваний, тестований та архітурно коректний код на Python;
• проєктувати та реалізовувати сервіси на Python/FastAPI для роботи з LLM, RAG, агентними системами та workflow-автоматизацією;
• інтегрувати пропрієтарні моделі через API: OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, Mistral, OpenRouter та інші затверджені компанією сервіси;
• піднімати, налаштовувати та підтримувати local-hosted моделі через vLLM, Ollama, llama.cpp, TGI або аналогічні інструменти;
• підбирати моделі під конкретні бізнес-завдання з урахуванням якості, швидкості, вартості, latency, context window, доступності, безпеки та стабільності результату;
• налаштовувати параметри інференсу моделей: temperature, top_p, max tokens, context window, batching, streaming, concurrency, rate limits, retry logic;
• реалізовувати vector RAG-пайплайни: завантаження даних, очищення, chunking, embeddings, індексація, retrieval, reranking, генерація відповіді, оцінка якості;
• реалізовувати graph RAG / knowledge graph RAG-підходи для завдань, де потрібна робота зі зв'язками між сутностями, документами, користувачами, договорами, клієнтами або бізнес-процесами;
• работувати з векторними базами даних: Qdrant, Weaviate, Pinecone, Chroma, FAISS, pgvector;
• работувати з графовими базами та інструментами: Neo4j, NetworkX, GraphRAG-підходи;
• будувати агентні системи: single-agent, multi-agent, planner-executor, supervisor, tool-using agents, human-in-the-loop flows;
• реалізовувати tool/function calling, structured outputs, JSON schema validation, retry/fallback logic та безпечну обробку відповідей моделей;
• інтегрувати агентні системи з корпоративними інструментами: CRM, бази знань, документи, пошта, календар, Slack/Teams, Jira, Confluence, Google Drive, Microsoft 365, внутрішні API;
• проєктувати та впроваджувати AI workflow automation на базі n8n: webhooks, triggers, AI Agent nodes, HTTP nodes, credentials, error workflows, черги, інтеграції з зовнішніми системами;
• розробляти eval/regression-фреймворки для перевірки якості LLM-відповідей, промптів, RAG та агентних сценаріїв;
  налаштовувати observability та моніторинг ШІ-систем: Langfuse, LangSmith, PromptFoo, Ragas, DeepEval, Arize Phoenix, Helicone, OpenTelemetry;
• контролювати ключові метрики: latency, cost per request, token usage, groundedness, hallucination rate, relevance, accuracy, refusal rate, error rate, regression rate;
  проводити технічні експерименти з моделями, промптами, RAG-підходами, агентними патернами та параметрами інференсу;
• брати участь у завданнях з fine-tuning / model adaptation за наявності потреби: підготовка датасетів, навчання, валідація, порівняння результатів з baseline;
• спільно з Prompt Spec Engineer впроваджувати prompt/spec architecture в код, забезпечувати версіонування промптів, трасування, тестування та контроль якості;
• спільно з Product Manager переводити продуктові вимоги в технічну архітектуру, API-контракти, схеми даних та план реалізації;
• забезпечувати безпеку ШІ-сервісів: захист від prompt injection, контроль доступу, фільтрація чутливих даних, логування, аудит, безпечне зберігання секретів;
• підтримувати сервіси в production: аналізувати інциденти, виправляти помилки, оптимізувати продуктивність, знижувати вартість запитів, покращувати стабільність.

Що компанія пропонує:

• працюємо в сучасному та стильному офіс в ближньому центрі;
• безкоштовний корпоративний трансфер (дім-офіс-дім);
• забезпечуємо якісним та смачним кейтерингом прямо в офісі;
• корпоративні заняття йогою та медитацією + корпоративні знижки на здорове харчування;
• WELCOME bonus - фінансовий бонус від компанії на початку співпраці;
• релокейт пакет (оплата квитків у Львів + перший та останній місяць + рієлторські послуги).

Cargofy is building the AI-native operating system for global freight. Cargofy deploys autonomous AI agents that automate procurement, dispatching, negotiations, tracking, and operations for carriers, shippers, and 3PLs across the US, Europe, and Asia. Our platform processes 100,000+ loads per day and hundreds of thousands of real-time events — replacing manual logistics with fully autonomous AI.

We need an AI Creator to put product video on a production line and make Cargofy as recognizable on screen as it is in the market.

Why This Role Exists Now

Logistics operators deciding whether to trust an autonomous agent with their dispatching don't want to read about it. They want to see it run. We ship fast — agents go to production in weeks. The old video model can't keep pace. So we're doing it the way a company that sells digital workers should: with AI. Your job is to turn that into an engine.

What Winning Looks Like

• Product video ships on the same clock as the roadmap, not weeks behind it
• Every launch has a clip; sales has assets that close instead of explain
• Cargofy has a recognizable visual language across every channel — not a folder of one-offs
• A library of reusable templates and workflows so production scales without starting from zero

What You Will Own

• Product videos — 30–60 second clips showing what Cargofy actually does, script to finished clip, end to end
• Channel distribution — cut one source into ready versions for LinkedIn, YouTube, events, and ads
• Content coverage — feature releases, product launches, case studies, sales and onboarding assets
• Production system — templates and workflows that scale output without scaling headcount
• Visual language — iterate on tone, visuals, and pacing until the look is unmistakably Cargofy

Who You Are

• Already making strong short-form video with AI tools — Runway, Kling, Veo, HeyGen, Pika, ElevenLabs, or similar
• Show the work rather than describe it
• Understand B2B product marketing — what a clip is for, not just how it looks
• Deliver finished videos, not source files for someone else to finish
• Experience with AI video for SaaS or tech products is a plus

Why Cargofy

• A clear mandate to build with AI, not against a headcount cap
• Fast cycles — script to brief to clip in days, not weeks
• Direct line to the team and the product, with real ownership of the output
• Contract, part-time / project-based to start — full-time on the table if it's a match
• Remote-first with offices in Kyiv, Warsaw, and Houston

🚀 We are looking for a Middle AI Engineer to build scalable AI-powered applications, developer tools, and workflow automation solutions that improve engineering productivity and deliver measurable business value.

This role is ideal for a strong Python engineer with hands-on experience building and deploying LLM-based applications. You will work on solutions involving RAG, AI agents, model integrations, backend services, and AI evaluation.

🎯 Responsibilities

- Design, build, and deploy LLM-powered applications from prototype to production.

- Develop AI agents, tool-using workflows, and multi-step automation solutions.

- Build and improve RAG pipelines using internal and third-party data sources.

- Integrate commercial and open-weight models such as OpenAI, Anthropic Claude, Google Gemini, and Llama-based models.

- Develop AI-powered capabilities such as code assistance, document generation, summarisation, knowledge search, and workflow automation.

- Build backend services, APIs, integrations, and data flows using Python.

- Implement embeddings, vector search, reranking, structured outputs, and tool calling.

- Evaluate AI outputs and improve relevance, accuracy, reliability, latency, and cost efficiency.

- Add logging, monitoring, error handling, fallback mechanisms, and output validation to AI systems.

- Deploy and maintain AI services in cloud environments.

- Collaborate with product, engineering, and business teams to deliver production-ready AI solutions.

🧩 Requirements

- 3+ years of commercial Python development experience.

- Hands-on experience building and deploying LLM-powered applications.

- Practical experience with at least one of the following:

  - RAG systems;

  - AI agents;

  - multi-step LLM workflows.

- Experience developing backend systems, REST APIs, integrations, and data flows.

- Experience with FastAPI, Django, Flask, or another Python backend framework.

- Hands-on experience with LangChain, LangGraph, LlamaIndex, or a similar AI orchestration framework.

- Experience working with OpenAI, Anthropic, Gemini, Llama-based models, or similar technologies.

- Practical understanding of prompt design, structured outputs, tool calling, and output validation.

- Experience with embeddings, vector search, and at least one vector database.

- Understanding of RAG concepts such as chunking, retrieval, reranking, and response generation.

- Experience evaluating and improving LLM output quality.

- Experience with Docker, Git, automated testing, and CI/CD workflows.

- Experience deploying applications to AWS, Azure, GCP, or another cloud platform.

- Understanding of common production AI challenges, including hallucinations, latency, rate limits, reliability, and cost control.

- English level: Upper-Intermediate or higher.
 

🧩 Nice to Have

- Experience with multi-agent systems.

- Experience building custom tools for AI agents.

- Familiarity with MCP servers, clients, or MCP-based integrations.

- Experience with LLM evaluation and observability tools such as LangSmith, Langfuse, Arize Phoenix, or similar.

- Experience with advanced retrieval techniques, hybrid search, or retrieval evaluation.

- Experience working with open-weight models and model-serving solutions.

- Familiarity with AI safety, guardrails, prompt injection protection, and responsible AI practices.

- Experience with Kubernetes or scalable AI service deployment.

- Experience building AI-powered developer tools.

🧩You'll have an opportunity to:

Build production-ready AI applications used by real users.

Work with modern LLM technologies, AI agents, and RAG systems.

Collaborate with experienced international engineering teams.

Gain hands-on experience with rapidly evolving AI frameworks and cloud platforms.

Solve challenging engineering problems and contribute to scalable AI solutions.

Continuously grow your expertise in Generative AI and modern software engineering

About the company: United Tech is a global IT product company shaping the future of real-time social connection. With millions of users across North America, Europe, LATAM, and MENA, we build next-gen mobile and web apps for live-streaming and social networking. Our platforms enable connection at scale fast, interactive, and deeply engaging. The market is projected to exceed $206B by 2030, and we are already leading the evolution. Founded in Ukraine, scaling worldwide
 

About your role: We’re looking for an AI Engineer who wants to build production-grade AI systems, not just experiment with demos. This is a high-ownership, greenfield role where you’ll help build our AI platform from scratch, working directly with the Head of AI.  You’ll own initiatives end-to-end — from discovery and architecture decisions to production delivery — and play a key role in shaping how AI is used across the company.

You’ll work with a modern AI stack including Claude/OpenAI/Bedrock, RAG pipelines, eval frameworks (Promptfoo, Ragas, LangSmith), and agentic patterns. This role is ideal for someone who enjoys autonomy, strong engineering ownership, and proactively identifying the next highest-leverage opportunity to drive into production.

In this role, you will:

• Design and build RAG and automation workflows inside our VPC environment.
• Create integrations across engineering, communication, and knowledge-management systems (Jira, Confluence, Slack, Google Drive, Git).
• Develop AI-powered pipelines for code quality checks, ticket auto-classification, documentation updates, release notes, and meeting summaries.
• Work on prompt engineering, model selection and routing (Haiku / Sonnet / Opus), and workflow optimization.
• Build evaluation and regression frameworks for AI workflows to prevent model-upgrade regressions.
• Create lightweight dashboards for engineering leads to surface delivery patterns and bottlenecks.
• Own initiatives end-to-end: scope → design → ship → measure → iterate.

It’s all about you:

• 5+ years of production experience with Python (services, async, API integrations, clean and testable code).
• Hands-on experience with LLM APIs in production: Anthropic Claude (Opus/Sonnet/Haiku), OpenAI, AWS Bedrock.
• Strong understanding of RAG systems: chunking, embeddings, hybrid search, re-ranking, grounding/citations.
• Prompt engineering + structured outputs + tool/function calling.
• Experience with eval/regression frameworks: Promptfoo, Ragas, LangSmith, or custom evaluation harnesses.
• Agentic patterns: ReAct, function-calling loops, fallbacks.
• Vector databases: pgvector, Pinecone.
• Workflow orchestration: n8n, Airflow, or custom orchestration systems.
• Observability: token-spend tracing, latency monitoring, model routing.
• Solid AWS fundamentals: IAM, Lambda, S3, CloudTrail — comfortable deploying inside VPC environments independently.
• CI/CD + pipeline hooks (GitLab/GitHub), webhooks, event-driven architectures.
• Basic SQL skills for analytical queries and metrics dashboards.
• Driver mindset: proactively identifies the highest-leverage opportunity and drives it to production
• B2 English level

Would be a plus:

• Real REST integrations with 2–3+ APIs
• Experience with workflow orchestration tools.
• Prompt evaluation tooling such as  LangSmith is nice to have
• Experience with Docker
• RAG experience: embeddings and vector stores (pgvector, Pinecone, or similar) for knowledge base use cases

What we offer

Care and support: 

• 20 paid vacation days, 15 sick days, and 6 additional days off for family events
• Up to 10 additional days off for public holidays
• 100% medical insurance coverage
• Sports and equipment reimbursement
• Team building events, corporate gifts, and stylish merch
• Financial and legal support
• Position retention and support for those who join the Armed Forces of Ukraine
• Participation in social initiatives supporting Ukraine
   

Comfortable working environment:

• Work from our Kyiv hub or remotely with a flexible schedule 
• Modern equipment or depreciation of your own tools
   

Investment in your future:

• Collaborate with a highly-skilled team of Middle & Senior professionals, sharing practical cases and expertise in the social networking niche
• 70% of our heads and leads have grown into their roles here – so can you!
• Performance-oriented reviews and Individual Development Plans (IDPs)
• Reimbursement for professional courses and English classes
• Corporate library, book club, and knowledge-sharing events
   

Hiring process:

• Intro call
• Technical Interview
• Final Interview
• Reference check
• Offer

Full-time
6months+
EU Only

About the Client
A major North American energy company — one of the largest players in energy infrastructure and distribution on the continent. The organization is investing in AI to modernize how it operates at scale, building LLM-based agentic systems to support decision-making and automation across a complex, data-rich environment. You'll join a team applying cutting-edge AI engineering to real production problems in a large, established enterprise.

Overview
You'll work on an AI/LLM platform built around agentic systems — designing, orchestrating, and hardening multi-agent workflows in production. This is a deeply hands-on engineering role focused on making LLM-based systems reliable, fast, and measurable, not just wiring up prompts.

Responsibilities

• Agentic systems: Build and operate multi-agent architectures — agent roles, triggers, handoffs, orchestration.
• RAG pipelines: Set up, tune, and fine-tune RAG systems end-to-end — chunking strategies, retrieval quality, iteration.
• Reliability engineering: Implement self-healing behavior — infinite-loop mitigation, graceful failures, clean handoffs between agents.
• Memory systems: Design and implement shared-memory concepts, with emphasis on fast memory.
• Evaluation & KPIs: Build evaluators of LLM performance, define KPIs, and automate evaluation (golden images, sample sizing).
• Performance & cost: Apply caching and token-optimization strategies to keep systems fast and economical.

Who You Are

• A hands-on builder who's actually shipped agentic/LLM systems, not just experimented with them
• Rigorous about reliability and measurement — you treat evals and failure modes as first-class work
• Comfortable owning ambiguous, fast-moving AI infrastructure end-to-end

Tech Stack You'll Work With
Core: Python, LLM tooling (llama / ollama), CUDA
AI systems: Multi-agent orchestration frameworks, RAG pipelines, LLM evaluation tooling
Concepts: Shared/fast memory, self-healing patterns, caching & token optimization, chunking strategies
Cloud: Azure (strong advantage)

Qualifications
Must-Have

• Strong hands-on experience with agentic systems (multi-agent orchestration, agent roles, triggers, handoffs)
• Strong hands-on experience building, tuning, and finetuning RAG pipelines (incl. chunking strategies)
• Experience with LLM performance evaluation — building evaluators, defining KPIs, automating them
• Self-healing / resilience implementation (loop mitigation, graceful failures, handoffs)
• Shared memory concepts and implementation, especially fast memory
• Caching and token-optimization strategies
• Python; llama/ollama; CUDA

Nice-to-Have

• Azure (products relevant to the above) — strong advantage
• Mistral — strong advantage
• Golden images and sample-size methodology for evaluation

The Role
Our standard delivery model splits work across a business analyst, a system analyst and team lead, developers, and testers. We are looking for a rare engineer who can own that entire cycle end to end — gathering requirements, shaping the technical solution and specification, building it across backend, frontend, and mobile, and testing it properly — using modern AI tooling as the force multiplier that makes this possible.

This is not a "prompt engineer" role and it is not a traditional full-stack role. It is a full-cycle builder who has genuinely internalized AI-assisted development: someone who uses coding agents, LLM APIs, and AI-driven testing daily to compress what used to take a team into what one strong, AI-fluent engineer can deliver — without sacrificing quality, security, or maintainability.

You will work directly with the CTO and product stakeholders, take an idea from a vague business need to a shipped, observable, production-grade feature, and set the standard for what AI-native engineering looks like at Billease.

WHAT YOU'LL DO:
Discovery & requirements
- Engage business stakeholders directly to elicit, clarify, and document business requirements.
- Turn ambiguous problems into clear, testable requirements — using AI to accelerate research, drafting, and gap analysis.

Solution design & specification
- Produce technical solutions and specifications: data models, API contracts (OpenAPI), service boundaries, sequence and architecture diagrams.
- Make sound architecture decisions aligned with our microservices, event-driven, and security-first principles, and document the trade-offs.

Build (backend / frontend / mobile)
- Implement backend services in Python and/or Golang on PostgreSQL, with appropriate messaging (Kafka) and caching.
- Build web frontends (Vue/Nuxt or React) and, where needed, mobile features (Android/Kotlin, iOS/Swift).
- Write reusable, testable, efficient code, using AI coding agents to move fast while keeping the codebase clean and reviewable.

Test & quality
- Design and execute meaningful test coverage — unit, integration, API, and end-to-end — leveraging AI-assisted test generation and review.
- Own quality end to end: you are the developer *and* the tester, and "done" means verified.

Ship & operate
- Deliver through our CI/CD pipelines and infrastructure as code.
- Build observability in at design time (metrics, logs, tracing) and support what you ship.

MUST-HAVE QUALIFICATIONS:
Mandatory: demonstrable, hands-on experience with a range of AI tools in real development work. This is the non-negotiable core of the role — we will ask you to show concrete examples of what you have built and how AI changed your workflow and output.

Real, current experience with AI development tooling, such as:
- AI coding agents / assistants (e.g. Claude Code, Cursor, Copilot, or similar) used to deliver production work, not just experiments.
- Working directly with LLM APIs (e.g. Anthropic, OpenAI) — prompt design, structured outputs, tool/function calling, and agentic workflows.
- AI-assisted testing, code review, documentation, and requirements/spec generation.

Full-cycle software engineering experience — you have personally taken features from requirement to production, not only implemented tickets handed to you.
- Backend proficiency in Python and/or Golang, with solid PostgreSQL and SQL skills.
- Frontend capability with a modern framework (Vue/Nuxt or React).
- Ability to write clear specifications and communicate effectively with both technical and non-technical stakeholders.
- Understanding of microservices, REST/API design, event-driven patterns, Git-based workflows, and CI/CD.
- Security- and quality-conscious mindset; comfortable owning testing and operability of your own work.
- Self-directed, high-ownership, and able to deliver under real deadlines.

NICE-TO-HAVE
- Fintech / financial services experience — lending, payments, digital banking, or regulatory-aware development (a strong plus).
- High-load / high-scale systems experience — performance tuning, partitioning, caching strategies, and designing for reliability under load.
- Mobile development experience (Android/Kotlin, iOS/Swift).
- Cloud-native and DevOps exposure: AWS, Kubernetes, Terraform/Ansible, Kafka, and observability tooling (Prometheus, Grafana).
- Experience integrating AI features into products (chatbots, voice, document/image processing, RAG, or automation).

HOW WE WORK
- Stack: AWS, Kubernetes, Python & Golang, PostgreSQL, Kafka, Vue/Nuxt & React, Android/iOS, Terraform/Ansible, GitLab CI/CD.
- Principles: clear and maintainable architecture, security-first, scalability and reliability, high observability and automation, cloud-native and microservices-oriented design, and a strong focus on performance, resilience, and operational excellence.
- AI-native by default: we expect AI to be part of how you think, design, build, and test — used deliberately and responsibly.

WHAT SUCCESS LOOKS LIKE:
Within your first few months, you independently take a real business need through the full cycle — requirements, spec, implementation across the relevant layers, tests, and a production release with observability — at a speed and quality that would traditionally have required a small team, and you help the rest of engineering raise its AI fluency along the way.

We're building a product in the AdTech space, where AI is becoming a core part of the platform - from creative automation to advertising performance analytics. We're now forming a new direction: a team that will systematically bring AI solutions into the product and work cross-functionally with other engineering teams.

About the Role

We're looking for an AI Engineer who can become the core of the company's future AI team. This is not a research role, and it's not about training your own models it's about real, hands-on implementation of LLM-based solutions in the product, with a strong focus on product ownership and engineering quality.

You will:

• Implement AI features directly in the product (e.g., AI-powered regeneration of ad creatives, AI scoring of creative performance, analysis of how editors interact with ads)
• Work cross-functionally with other engineering teams, helping to systematize AI usage across the company
• Design and build LLM-based solutions: model API integration, prompt engineering, RAG, AI agents/tooling
• Proactively propose where and how AI can strengthen the product — rather than waiting for a fully defined spec
• Help lay the foundation for a dedicated AI team within the company over time
   

What Matters to Us

• A strong engineering background (backend/full stack), with production development experience — not just notebook experiments
• Hands-on experience integrating LLMs into real products (OpenAI, Anthropic, or similar APIs) — NOT experience training or fine-tuning your own models
• Understanding of prompt engineering, RAG, and building AI agents
• Product ownership: someone who generates their own ideas for how AI can move the product forward, and sees them through to implementation
• A genuine drive to implement solutions, not just conceptualize them

Who This Role Is For

• An engineer who is passionate about not only exploring or discussing AI approaches but also implementing AI solutions directly into production products.
• A professional with a strong engineering foundation who enjoys writing high-quality code and seeing its tangible impact on the product.
• A hands-on engineer who thrives on building and taking ownership rather than managing people. Someone who is eager to take end-to-end responsibility for designing, implementing, and continuously improving their product area.

About the Role:

We are looking for an experienced AI Transformation Lead to drive the adoption of Generative AI across our global organization. This is a strategic, cross-functional role focused on helping business and technology teams integrate AI into their daily workflows, improve productivity, and establish AI best practices.

This is not a Machine Learning or AI Engineering role. Instead, you will be responsible for identifying opportunities where AI can create business value, introducing modern AI tools and workflows, enabling teams through training and best practices, and embedding AI into everyday operations.

Key Responsibilities:

• Drive company-wide AI adoption initiatives across business and technology teams.
• Identify opportunities to improve business processes through AI-powered solutions.
• Evaluate, recommend, and implement modern AI tools and workflows.
• Develop AI best practices, prompting standards, governance, and usage guidelines.
• Deliver AI training sessions, workshops, and enablement programs.
• Partner with Functional Architects, Software Engineers, QA, DevOps, Project Managers, and Customer Support teams to maximize AI adoption.
• Continuously monitor emerging AI technologies and recommend innovative solutions.

• Measure AI adoption, business impact, and continuously optimize AI-enabled processes.

   

How You Will Make an Impact:

You will help different teams leverage AI to improve efficiency and quality, including:

Functional Architecture

• User Stories
• Functional Specifications
• Business Documentation
• User Manuals

Software Engineering

• AI-assisted code generation
• Code reviews
• Refactoring
• Documentation
• Architectural guidance

Quality Assurance

• Test case generation
• Regression test suites
• Test data generation
• Test automation improvements

DevOps

• Infrastructure scripting
• Deployment automation
• Monitoring and troubleshooting
• Operational runbooks

Project Management

• Meeting summaries
• Project planning
• Status reporting
• Action tracking

Customer Support

• Knowledge base creation
• Troubleshooting guides

• Customer communications

   

What We’re Looking For:

• Proven experience leading AI adoption, digital transformation, or process improvement initiatives.
• Strong understanding of Generative AI technologies and their practical business applications.
• Hands-on experience with AI productivity tools such as ChatGPT, Microsoft Copilot, GitHub Copilot, Claude, Gemini, Cursor, Perplexity, or similar platforms.
• Ability to identify automation opportunities and translate AI capabilities into practical business solutions.
• Experience designing AI-enabled workflows across multiple business functions.
• Excellent communication, facilitation, and stakeholder management skills.
• Strong understanding of Software Development Life Cycle (SDLC), Agile methodologies, and modern software engineering practices.

• Experience collaborating with cross-functional teams in technology-driven environments.

   

Nice to Have:

• Background in Software Engineering, Solution Architecture, Engineering Management, Technical Consulting, or Digital Transformation.
• Experience implementing enterprise AI initiatives.
• Experience developing AI governance frameworks and responsible AI practices.

• Familiarity with Microsoft 365, Atlassian, Google Workspace, or similar enterprise collaboration platforms.

   

If you are passionate about helping organizations transform the way they work through AI, we would love to hear from you.

Project Description

This team is working with an AI chip company that develops specialized software-centric Machine Learning Systems-on-Chip (MLSoC) to enable high-performance, power-efficient "Physical AI" at the edge. Their technology is designed for robotics, autonomous vehicles, and smart cameras, allowing them to process complex computer vision and multimodal AI tasks locally with very low power consumption.

Job Description
The ideal candidate is an ML engineer with a strong background in autonomous systems and mobile robotics. The role requires someone who has spent meaningful time training ML models for complex autonomous platforms—focusing on perception, navigation, and control in GPS-denied, high-speed, and visually complex environments.

The candidate must have hands-on production experience with NVIDIA Isaac Sim and the Omniverse ecosystem, including building photorealistic simulation environments, running Omniverse Replicator synthetic data pipelines for perception, and closing the sim-to-real gap on real robotic hardware. Experience with reinforcement learning or imitation learning for navigation policies is required—specifically training agile navigation policies in simulation and validating them on hardware. A background from an autonomous robotics company, a research group, or an embodied AI team is a must.

On the ML side, the candidate will own the full perception model pipeline: training and fine-tuning visual-inertial odometry components, stereo depth estimation, and defect detection models on synthetic sensor data generated in Isaac Sim; quantizing those models to INT8/BF16 for deployment on the customer MLA; and profiling MLA utilization across navigation, inspection, and complex tracking use cases. The candidate will be the first to apply the custom Palette toolchain to a robotics AI stack—a genuinely novel workload requiring comfort navigating a custom MLA compiler with limited prior art. Strong PyTorch proficiency, familiarity with autonomous control stack integration, and a demonstrated track record of deploying perception models on real-world hardware are essential.

Job Responsibilities

We are seeking an exceptional Senior/Principal Engineer, Robotics Software to join our cutting-edge Robotics and AI software team. The ideal candidate brings deep, hands-on experience in autonomous mobile robots (AMRs), aerial robots. 

• Drive technical discussions relating to architecture, design, and development of software and hardware systems for AMRs, and/or humanoid robots.
• Build and deploy perception, localization, and navigation systems using Lidar-based and Visual SLAM techniques.
• Integrate deep learning models for object detection, motion planning, behavior prediction, and scene understanding.
• Contribute to or lead R&D efforts related to Generative AI and its applications in robotics.
• Collaborate with cross-functional teams including electrical engineers and AI scientists to deliver complete solutions.
• Mentor junior engineers and provide strategic input into long-term robotics and AI vision.

We are an international developer of IT products for medicine and related services. Our products are used by more than 5,000 doctors and more than 1,000 clinics in Europe.

The goal is to attract the largest possible audience to the main projects - clinics and doctors up to 3 million. As well as expanding unique traffic to 10 million per day until 2025. We are looking for AI Engineer / AI Architect with deep knowledge of Fundamental Mathematics, Probability Theory and Bayesian Statistics, Graph Algorithms, Mathematics of Transformers, Hybrid AI Approaches, Uncertainty Assessment.

Must-have skills are:
1. ML and Data Science (Core):
- Expert in NLP & LLM: Deep understanding of Transformers architecture (BERT, RoBERTa, GPT, T5, Llama/Mistral). Experience with Large Language Models (LLM) – not prompt engineering, but fine-tuning (PEFT, LoRA) of open-source models on specific datasets.
- RAG (Retrieval-Augmented Generation): Experience building RAG systems is required. The AI ​​must be able to search for information in different data sources.
- Named Entity Recognition (NER) & Information Extraction: Ability to extract specific entities from unstructured text.
- Tabular Data & Classic ML: Confident use of classical algorithms (XGBoost, CatBoost, LightGBM) for analyzing structured data and building probability scoring models.
- Frameworks: PyTorch (preferred) or TensorFlow/Keras. Proficiency in HuggingFace, Scikit-learn, Pandas, and NumPy.

2. MLOps:
- Experience wrapping models in APIs (FastAPI, Flask) with minimal latency. 

- Docker knowledge and experience working with local GPU instances are required.
- Experience working with vector databases (Pinecone, Milvus, Qdrant, ChromaDB) for quickly searching for similar cases or working with knowledge bases.

- ML Pipelines: Experience building model training and retraining pipelines (Airflow, MLflow, DVC).

3. MedTech and Security:
- Practical working experience with sensitive personal data and GDPR/DSGVO requirements.

- It is important to be able to deploy and optimize local models (Llama 3, Mistral, Mixtral) within a closed company environment to ensure data privacy.
- Skills in quantization and distillation of models to speed up work without significant loss of quality.

Nice-to-have skills:
- Experience working specifically with medical data (EHR, EMR; HL7, FHIR, ICD-10/11 standards). 

- Knowledge of Interpretability/Explainable AI (XAI) methods. The end user should understand why the AI ​​suggested a particular option (SHAP, LIME).

- Experience with multimodal models.

What we offer:
- Paid holiday for 28 working days.
- Transparent incentive system.

Working with us means:
- Unique technological and innovative projects.
- Gaining experience in modern western world practices in security, scalable architecture, etc.
- In-depth knowledge of telemedicine.
- Working with Big data, high load, distributed computing and data buses.
- Socially important projects.
- Opportunity to influence both technology and product solutions.
- Profitability and stability of a large European company.

🔹 100% remote | 🌎 Global team | ⏳ Full-time

NineTwoThree AI Studio is a premier product design, engineering, and marketing firm specializing in custom AI, web, and mobile applications for established brands and funded startups. We are based in Massachusetts but with an American and European staff and a strong, collaborative remote culture.

We’re a team that loves doing good work with great people. Our relatively small size keeps us fast and nimble. The wealth of knowledge, experience and talent paired with proven recipes and best practices allows us to find opportunities to help new products succeed.

With a portfolio of over 150 launched products over 13 years, NineTwoThree has garnered recognition as a top AI agency in the U.S., earning accolades such as inclusion in the Inc. 5000 list for four consecutive years and being named among the top 50 AI firms alongside industry leaders like Microsoft, NVIDIA, and IBM. We’ve built AI and ML tech for big brands like Consumer Reports, FanDuel, and Nara, as well as startups in legal tech, logistics, education, and more. 

Role Overview

As a Senior ML Engineer at NineTwoThree AI Studio, you will sit at the intersection of production-grade software engineering, advanced natural language processing, and client delivery. We build custom, high-impact AI systems for brands and startups across diverse industries (such as healthcare, logistics, and fintech).

Instead of siloed academic research, this role demands a product-minded builder. You will design, optimize, and deploy robust LLM applications, custom predictive analytics, and agentic workflows directly into our clients' software ecosystems, taking absolute ownership of features from prototype to production.

Technology Stack

• Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs.
• Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches.
• Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines.
• Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection).
• Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines.
• Languages: Production-grade Python.

Responsibilities

• Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines.
• Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment.
• Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints.
• Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy.
• Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection).
• Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams.
• Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.

Technical Experience

• Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing.
• LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. Ability to identify when a deterministic solution is preferable to an LLM or agent-based solution.
• Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement.
• Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. Ability to reason about tradeoffs between quality, latency, cost, reliability, and engineering complexity.
• Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently.
• Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS.
• Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support.

Product & Team Capabilities

• Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks.
• The "Product Engineer" Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built.
• Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts.
• Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).

What We Offer

• Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years
• Paid sick leave, 10 national holidays, and 2 company days off
• Well-being budget
• Global health coverage 
• Maternity/paternity leave
• Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager)
• Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor

What's The Process

We value your time and ours and make the process fast and easy. Our interview process takes the following steps: a short interview with the HR, 2nd technical interview with ML Engineer and CTO (optional), 3rd live-coding interview, Offer.

Location: London, UK (relocation is expected)

Employment: Full-time
 

We are looking for a Founding Engineer to join an early-stage AI startup building automation solutions for the Testing, Inspection, Certification, and Compliance (TICC) industry.
 

As one of the first technical hires, you will work directly with the CTO, helping to build the company's core AI platform while working closely with customers to automate real operational workflows.
 

Responsibilities:

• Design, develop, and deploy end-to-end software solutions.
• Work directly with customers to understand their business processes and identify automation opportunities.
• Build AI-powered solutions using LLMs, computer vision, workflow automation, and integrations with third-party systems.
• Develop features from idea to production with a strong focus on business impact.
• Integrate with legacy software, sensors, laboratory equipment, and external APIs.
• Contribute to the architecture of the core platform.
• Help shape engineering processes and grow the engineering team as the company scales.
   

Requirements:

• Strong software engineering background with the ability to work across different technologies and tech stacks.
• Experience building and shipping production software independently.
• Product mindset with the ability to solve business problems through technology.
• Excellent communication skills and ability to work directly with customers.
• Comfortable taking ownership and working in a fast-paced startup environment.
• Willingness to travel across the UK (2–3 days per week during customer onboarding phases).
   

Nice to Have:

• Production experience with AI/ML (LLMs, AI agents, Computer Vision, Data Science).
• Experience with cloud platforms (preferably GCP).
• Previous startup or Founding Engineer experience.
• Experience migrating legacy systems.
• Consulting or Forward-Deployed Engineering background.
• Experience in compliance, inspection, logistics, facilities management, or industrial software.
• Experience working with non-technical stakeholders.

We are looking for a Middle AI Engineer who will help us build, integrate, and scale AI-powered products that solve real business challenges.

This role is focused on creating production-ready AI solutions — from backend services and LLM integrations to RAG systems, AI agents, and workflow automation. You will work closely with Product Managers, developers, and business teams to transform ideas into reliable AI products that deliver measurable value.

What you will do:

• Develop production-ready AI services, backend components, APIs, and integrations for internal and external AI products.
• Design and implement AI solutions based on Large Language Models (LLMs), RAG architectures, AI agents, and workflow automation.
• Build and optimize Python-based services and backend infrastructure with a focus on scalability, reliability, and maintainability.
• Integrate and work with different types of AI models — including proprietary APIs (OpenAI, Anthropic, Gemini, etc.) and locally hosted models.
• Design and improve RAG pipelines: data processing, embeddings, vector search, retrieval optimization, knowledge bases, and AI response quality improvement.
• Develop AI agent systems and automation workflows, including integrations with external APIs, corporate systems, and internal tools.
• Conduct technical experiments with models, prompts, architectures, and inference parameters to identify the most effective solutions.
• Implement AI quality evaluation and monitoring approaches: analyze model performance, improve reliability, reduce errors, and optimize system efficiency.
• Collaborate with Product Managers and cross-functional teams to translate product requirements into technical solutions and scalable architectures.

• Ensure AI services are secure, stable, and production-ready by monitoring performance, resolving issues, and continuously improving systems.

   

What we expect from you

• 3+ years of commercial software development experience with strong Python expertise.
• Experience building backend services, APIs, and integrations using modern technologies.
• Strong understanding of software engineering principles: clean code, architecture patterns, testing, scalability, and maintainable solutions.
• Practical experience developing AI products based on Large Language Models (LLMs).
• Hands-on experience with RAG systems, embeddings, vector databases, retrieval optimization, and AI application development.
• Experience working with AI model integrations, including external APIs and/or locally deployed models.
• Understanding of AI agents, workflow automation, and approaches to building reliable AI systems.
• Experience with Docker, CI/CD, and production deployment processes.
• Ability to analyze AI system performance and improve quality, efficiency, and reliability.
• Upper-Intermediate English level (B2+) 

Will be a plus:

• Experience with fine-tuning or adapting AI models.
• Experience with MLOps / LLMOps practices.
• Knowledge of NLP approaches.
• Experience with cloud platforms and scalable infrastructure.

What we offer

• Competitive compensation up to $4,000, depending on your experience and expertise.
• Office-based work in Lviv with the possibility of a hybrid schedule.
• High level of technical ownership and the opportunity to solve challenging engineering problems.
• Professional development opportunities: company-sponsored courses, training, and access to necessary tools and resources.
• A team of experienced specialists who value expertise, initiative, and high-quality engineering.
• The opportunity to become one of the key engineers shaping the company’s AI direction.
• Relocation support for candidates from other cities, including assistance with moving and adaptation in Lviv.