Jobs ML / AI

About us

We're building an AI boxing coach. A fighter films a round on their phone, we tell them what's wrong with their technique, and then we build them a training plan to fix it: specific drills targeting their specific faults. The kind of coaching you'd get from a good trainer, for people who mostly train alone.

We're a remote team of 4. Early stage, shipping fast, no process overhead.

Where we are

• A working punch classifier running on pose data
• 15 form rules live in production: feet apart, hip rotation, full arm extension, guard drop, elbow flare, and others

• An in-house labeling team producing our training data

   

What you'd work on

• Adding form rules. Some are geometry and heuristics on skeleton data, some need small models trained on our labeled clips. Knowing which is which is part of the job.
• Teaching the model defensive movement: slips, rolls, blocks. The current classifier is built around punches thrown from two hands, so head movement probably needs a different approach. Working out what that approach is would be yours.
• Improving the punch classifier over time: more classes, better accuracy.

• Owning the labeling spec. You decide what the labelers annotate and make sure what comes back is usable.

   

You

• 2+ years computer vision. Pose estimation or action recognition strongly preferred.
• Comfortable with temporal models over sequence data, not only static image work.
• For at least the first couple of months you'll be the main developer, as I'll be focused on product and partnerships. We'll talk regularly and I'll set the priorities, but how things get built is yours. Nobody is going to review your approach for you, and there's no existing playbook to follow.
• We work over video, camera on. Hours are flexible, but you're reachable and you show up to calls.

What we offer

• Real ownership of the CV work. You decide how things get built, not just implement someone else's spec.
• Competitive salary, paid monthly in USD
• Fully remote
• Flexible hours. We care about what ships, not when you're online.

How we hire

Three steps, and we move fast:

1. Application. Tell us two things. First, relevant experience: action or gesture recognition, pose or skeleton data, video pipelines. What you built, and what the numbers were. Second, why this job specifically.
2. Call. 20 minutes, video. We go through one of your projects properly and you ask us whatever you want.
3. Paid test task. Real data from our dataset, scoped to around 4 hours, flat fee paid on submission whether or not we move forward.

We are looking for a highly organized and detail-oriented AI Image Editing & Back Office Specialist to join our team.
 

This role combines AI-powered image editing with operational back-office support. You will receive requests through our help desk system, process AI image edits, and ensure every request is completed accurately and assigned to the correct ticket.
 

Success in this role depends on attention to detail, strong organizational skills, and the ability to manage a high volume of requests without mistakes.
 

Responsibilities

• Edit static images using AI face swap and image editing tools.
• Process requests received through the internal help desk system.
• Prepare edited images and return them to the appropriate support ticket.
• Maintain accuracy and consistency across all requests.
• Handle multiple tasks simultaneously while following established workflows.
• Perform back-office tasks within internal systems.
• Ensure all requests are completed correctly and on time.
 

Requirements

• Experience using AI tools for editing static images and face swaps.
• Strong attention to detail and excellent organizational skills.
• Previous experience with back-office or operational support.
• Comfortable working with ticketing/help desk systems.
• Ability to manage a high volume of repetitive tasks while maintaining accuracy.
• Self-motivated and able to work independently.
 

Nice to Have

• Experience with tools such as FaceFusion, Roop, InsightFace, ComfyUI, Stable Diffusion, Flux, Photoshop AI, or similar AI image editing software.
• Experience working in customer support platforms or help desk systems.
• Experience with workflow automation.
 

What We Offer

• Remote full-time position.
• Comprehensive salary
• Long-term opportunity.
• Work with modern AI technologies.

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

You will work on scalable AI solutions, developer-focused tools, workflow automation, and AI capabilities that improve engineering productivity and deliver real business value for clients.

Responsibilities:

— Design, build, and deploy LLM-based applications and agentic AI systems from prototype to production.

— Develop production-grade AI agents, multi-agent systems, and tool-using workflows.

— Implement RAG pipelines over proprietary and third-party data sources.

— Build and maintain vector databases for AI data storage, retrieval, and search.

— Design and implement AI-powered capabilities such as code assistance, documentation generation, test automation, summarization, knowledge discovery, and code review support.

— Evaluate foundation models such as OpenAI, Anthropic, Gemini, Llama, and other commercial or open-weight models.

— Build LLM evaluation and benchmarking pipelines, including offline tests, online monitoring, human-in-the-loop review, and quality measurement.

— Improve model performance across relevance, faithfulness, latency, throughput, reliability, and cost.

— Design and maintain APIs, backend services, integrations, data flows, and custom tools for AI systems.

— Build custom tools, skills, integrations, and MCP-style server/client patterns for agentic workflows.

— Deploy, monitor, and optimize AI systems on cloud platforms, with a focus on scalability, reliability, observability, and cost efficiency.

Requirements: 

— 2+ years of hands-on experience building and deploying LLM-based applications, RAG systems, or AI agents in production.

— Strong Python experience, ideally 3+ years.

— Experience building backend systems, APIs, services, integrations, and data flows.

— Experience with object-oriented programming, backend architecture, design patterns, and production system design.

— Hands-on experience with LangChain, LangGraph, or similar AI orchestration frameworks.

— Strong experience designing agentic AI systems, tool-using agents, or multi-step AI workflows.

— Experience with RAG architecture, including chunking strategies, embeddings, vector databases, reranking, and retrieval evaluation.

— Experience with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, Milvus, or similar.

— Experience with LLM evaluation and benchmarking, including designing evaluation pipelines and measuring model performance.

— Strong prompt design, prompt iteration, and prompt optimization skills.

— Experience working with major model providers or open-weight models such as OpenAI, Anthropic, Gemini, Llama, or similar.

— Cloud experience with AWS, GCP Vertex AI, Azure AI Foundry, or similar platforms.

— Familiarity with AI safety, responsible AI, guardrails, and output validation for production systems.

— Experience with CI/CD pipelines, deployment workflows, and engineering best practices.

We offer:

— remote time job, B2B contract
— 12 sick leaves and 18 paid vacation business days per year
— Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
— Smart environment
— Flexible work schedule
— Competitive salary according to the qualifications

About us:

EveryMatrix is a leading B2B SaaS provider delivering iGaming software, content and services. We provide casino, sports betting, platform and payments, and affiliate management to 200 customers worldwide.

But that's not all! We're not just about numbers, we're about people. With a team of over 1000 passionate individuals spread across twelve countries in Europe, Asia, and the US, we're all united by our love for innovation and teamwork.

EveryMatrix is a member of the World Lottery Association (WLA) and European Lotteries Association. In September 2023 it became the first iGaming supplier to receive WLA Safer Gambling Certification. EveryMatrix is proud of its commitment to safer gambling and player protection whilst producing market leading gaming solutions.

Join us on this exciting journey as we continue to redefine the iGaming landscape, one groundbreaking solution at a time.

We are looking for a passionate and dedicated AI Engineer to join our team in Lviv!

About the job:

We’re looking for an AI/ML Engineer to support and build AI-powered solutions that enhance automation, analytics, and intelligent workflows across our products. You’ll work with modern LLMs, AI agents, retrieval systems, and scalable backend services, taking ideas from prototype to production in a fast-moving environment and building scalable solutions in a high-impact domain. The ideal candidate for us has strong software engineering fundamentals, practical experience with generative AI systems, and hands-on experience building robust pipelines and production-grade solutions.

What You'll get to do:

• Design, develop, and deploy AI-powered applications and services.
• Integrate LLMs and AI APIs into production systems.
• Build and optimize RAG pipelines, embeddings workflows, and vector search solutions.
• Develop AI agents and workflow automation systems.
• Work with structured and unstructured data for AI-driven use cases.
• Collaborate with Data Engineers and Backend Engineers on scalable infrastructure.
• Monitor AI system performance, quality, latency, and cost efficiency.
• Experiment with prompt engineering, fine-tuning, and model evaluation techniques.
• Participate in architecture discussions and contribute to technical decision-making.

What You need to know:

• Degree in Computer Science, Mathematics, Engineering, or equivalent field.
• 3+ years of hands-on experience in applied AI-engineering.
• Strong programming skills in Python.
• Experience working with LLMs and modern AI frameworks such as LangChain, LlamaIndex, or similar.
• Understanding of RAG architectures, embeddings, vector databases, and semantic search.
• Experience integrating external APIs and cloud-based AI services.
• Knowledge of REST APIs, microservices, and scalable backend architectures.
• Familiarity with Docker, CI/CD, and cloud platforms, especially GCP.
• Strong problem-solving skills and ability to work independently.
• Interest in AI automation, intelligent systems, and applied generative AI solutions.
• Experience in Python backend engineering will be a plus.
   

Here's what we offer:

• Start with 22 days of annual leave, with 2 additional days added each year, up to 32 days by your fifth year with us.
• 3 sick leave days per year, no doctor's note required; 30 medical leave days with medical allowance
• Hybrid work schedule is available after the first three months of employment with 80/20 office/home format until the end of 2026. Starting from 2027, the policy will transition to primarily office-based work, with up to 10 work-from-home days per year with manager approval.
• Benefit from two Free Fridays each year, limited to one per quarter.
• Daily catered lunch or monthly lunch allowance. 
• Private Medical Subscription. 
• Access online learning platforms like Udemy for Business, LinkedIn Learning or O’Reilly, and a budget for external training.
• Gym allowance.
• Corporate English lessons.
• Support for New Parents:
• 21 weeks of paid maternity leave, with the flexibility to work from home full-time until your child turns 1 year old.
• 4 weeks of paternity leave, plus the flexibility to work from home full-time until your child is 13 weeks old.

Our office perks include on-site massages and frequent team-building activities in various locations.

At EveryMatrix, we're committed to creating a supportive and inclusive workplace where you can thrive both personally and professionally. Come join us and experience the difference!

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.

Are you passionate about building intelligent systems that can think, act, and adapt? At Sigma Software, we work on innovative AI-driven solutions that push the boundaries of technology. In this role, you will design, build, and optimize cutting-edge AI deployment systems — from autonomous agents to complex orchestration layers — for both greenfield products and established platforms.

We are seeking a highly skilled AI Deployment Engineer to lead the implementation of advanced AI solutions in operational environments. This role requires strong customer-facing presence, exceptional advisory skills, and the ability to design and deploy AI systems that deliver measurable organizational impact.

Requirements

• Proven experience in AI/ML deployment and system engineering.
• Strong proficiency in AI frameworks.
• Expertise in large language models, retrieval-augmented generation, and AI agents.

• Excellent communication and stakeholder management skills.

   

  Responsibilities

• Collaborate directly with customers, providing strategic guidance and technical expertise.
• Own the vision for AI operations implementation, building end-to-end workflows and taking them to production.
• Translate complex business challenges into actionable technical plans.
• Rapidly prototype AI solutions to demonstrate possibilities to clients.
• Engineer production-grade AI solutions with deep knowledge of AI tooling, architecture, and deployment.
• Manage stakeholder alignment, proactively escalating issues and requesting support when necessary.
• Design and build LLM workflows, RAG pipelines, agentic systems, and custom data pipelines.

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.

At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed.

Now we are looking for a Software Development Engineer (MLOps) to join us as part of our team.

About the role

As a Senior ML Operations Engineer, you will support teams building and deploying AI-driven solutions, helping them overcome common challenges in the ML development, deployment, and support lifecycle. This role offers autonomy in decision-making, encouraging a proactive approach to solving complex issues and finding optimal solutions for scalable systems.

About the project

PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials and many more.

Team composition

1 Engineering Manager, 4 Senior ML Ops Engineers

Work Environment

The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology.

As a qualified expert, You will

• Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation.
• Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment.
• Provide mentorship, technical guidance, and perform code reviews for team members.
• Design and deliver on projects end-to-end with little to no guidance.
• Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC.
• Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities.
• Support the vision and values of the company through role modeling and encouraging desired behaviors.
• Participate in various cross-functional company initiatives and projects as requested.
• Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value.
• Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance.

We’re looking for you if you have

• Degree in Computer Science, Information Systems, Machine Learning, or a similar field preferred (or equivalent practical experience).
• 5+ years of hands-on software development experience with Python (Java experience with strong Python proficiency also considered).
• 4+ years of experience designing and building distributed software systems and architectures.
• 3+ years of hands-on experience deploying and operating Machine Learning services in production.
• Experience supporting ML lifecycle operations including post-deployment monitoring and maintenance.
• Experience in cloud-native stack, with a practical understanding of containerization technologies such as Kubernetes and Docker.
• Demonstrated experience with SQL and NoSQL database design and implementation.
• Ability to decompose complex problems into iterative, well-defined solutions.
• Strong problem-solving abilities with focus on building scalable, efficient, and maintainable systems.
• Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions.
• Ability to be a team player who can also work independently.
• Experience working across multiple development teams is a plus.

Desired Skills

• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).
• Proficiency in GitOps practices and CI/CD pipeline development and management.
• Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems.
• LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).
• LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).
• Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).
• RAG Systems: Experience building Retrieval-Augmented Generation systems and evaluating both retrieval quality and generation performance.
• Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML
• Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn
• Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka.
• ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow.
• API development with FastAPI or similar frameworks.
• Java programming experience is a plus.

Interview Process

1. Pre-Screening with the recruiter
2. Tech Interview(ML Fundamentals, Software Engineering) + Live Coding (up to 1,5 hours)
3. ML System Design Interview (up to 60 min)
4. Interview with Engineering Manager (60 min)
5. Final Client Interview (60 min)