Jobs ML / AI Engineer

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Senior Software Engineer to build and operate production AI agent and search services on top of a large-scale data platform. This is hands-on work at the intersection of distributed backend engineering and applied AI: multi-agent workflows, tool-execution infrastructure, retrieval and relevance, and integrations with multiple LLM providers — all running as real, customer-facing production services with the reliability expectations that implies.

Responsibilities

• Design and implement scalable multi-agent workflows (including conversation state), tool-execution infrastructure, and data-aware tools working with catalogs, schemas, lineage, SQL, jobs, semantic metadata, data preparation, and visualizations
• Build search and retrieval capabilities with OpenSearch or equivalent — indexing, relevance tuning, hybrid keyword + vector search
• Integrate multiple LLM/model providers with secure configuration and graceful degradation
• Develop APIs and event contracts across REST, streaming/SSE, gRPC, protobuf, and SDKs, including UI integrations
• Operate, monitor, and improve production services — service health, logs, metrics, traces, search-cluster health, latency, and error rates; participate in the team's production-support model
• Investigate incidents, drive root-cause analysis, and ship durable fixes
• Design for permissions, tool approvals, multi-tenancy, and customer-data governance
• Maintain dependencies, apply security fixes, and reduce tech debt; produce designs, tests, runbooks, and documentation; lead technical discussions and review code

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field — or equivalent practical experience
• 7+ years of relevant experience, or equivalent demonstrated senior-level impact
• Strong Java software engineering skills
• Strong grasp of concurrency, data structures, API design, testing, maintainability, and backward compatibility
• Solid background in distributed systems, microservices, cloud services, or data-intensive backends
• Hands-on experience with search technologies — OpenSearch, Elasticsearch, or Lucene — including vector search, embeddings, or relevance systems
• Experience building LLM applications, agent/tool workflows, RAG systems, or model-provider integrations
• Strong production instincts: diagnosing failures via logs, metrics, and traces; designing for fault tolerance, retries, cancellation, and recovery
• SQL and familiarity with data platform concepts: catalogs, metadata, schemas, governance, lineage
• Comfort leading ambiguous, cross-service work with clear stakeholder communication and strong written documentation
• Comfortable with AI-assisted development workflows — using modern AI tools productively while critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

Desired Skills

• LangChain4j or similar agent frameworks
• Python for AI services, evaluation harnesses, or sandboxed execution
• React and TypeScript
• gRPC, protobuf, Quarkus, Kubernetes, MongoDB, KV stores, event-driven architectures
• Apache Arrow, Apache Iceberg, OpenLineage, semantic layers, or data-governance systems
• AI quality evaluation: LLM-as-judge, model benchmarking, production AI metrics
• OpenSearch operations: index lifecycle management, relevance tuning, incident response
• Open-source contributions

Details

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

We're a Registered Anthropic Partner and an Official Advanced Tier AWS Partner, currently serving 100+ enterprises and startups across the US, EU, and other Tier 1 markets.
 

We're starting a new product, Tokensaver: unified access to AI models for companies that use AI heavily, with the explicit goal of cutting their AI bill. You'd be the first engineer on it, building from scratch. 

We are looking for a Founding AI Engineer who will build the "Tokensaver" product from scratch.

AI Router technology. This is the core of the product, so it should be the part you're most curious about. We expect candidate to have genuine interest in AI Router technology: model selection, routing logic, caching, fallbacks, and the cost/quality tradeoffs behind each. 

What we expect:

• 3+ years of practical software engineering experience
• You build with AI agents daily and use latest AI coding approaches in your daily workflow
• Tech stack: Node.js, TypeScript, Next.js, PostgreSQL, ClickHouse, basic knowledge of Go or Rust is a plus
• Hands-on experience with LLM APIs, agentic workflows, RAG, embeddings, structured outputs, prompt design, and where models break down
• Experience building or integrating MCP servers, APIs, connectors, and components
• Product-oriented thinking, understanding what to build - product judgment about what to ship and what to deliberately leave out
• Genuine interest in AI Router technology

What we offer and why it's great opportunity:

• Founding ownership. You're the founding engineering hire for Tokensaver product - you choose the architecture, the stack and decision on how to build. 
• Competetive base salary & equity compensation from day 1.
• Clear career growth. There is a clear path to becoming the technical leader of Tokensaver product and building the engineering team around you.
• Work on technology that is just being invented right now. AI routing and multi-model infrastructure are still at a very early stage, with best practices being defined in real time. You’ll work on intelligent model selection, caching, fallbacks, observability, and cost/quality/latency optimization at the frontier of modern AI infrastructure.
• Distribution & real Credibility behind the company from day one. We're a Registered Anthropic Partner and an Official Advanced Tier AWS Partner. Our existing business already serves 100+ companies across the US, Europe, and other Tier 1 markets, giving Tokensaver a strong base for early users, feedback, and distribution.

To apply, please answer 2 questions:

1) How do you use AI tools in your daily work? Be specific - which AI tools, for which parts of the job. 

2) Our goal is to build an AI Router Technology - unified access to AI models that cuts costs for businesses already spending heavily on AI. How would you approach building this from scratch?

We need a senior engineer who owns the question of whether our AI features actually work. About 70 percent of the job is quality and evaluation in agent-based development: harnesses, datasets, regression gates, and the standards around them. The other 30 percent is raising the quality of everyone else’s product work. You review code across the Node, React, and React Native apps, coach engineers on resilient design, and shape features before they are built. You write production code too, but to keep your judgment current, not as your primary output.

That stack is Node services, React and React Native front ends, PostgreSQL, and AWS. Playwright for web, Maestro for mobile, GitHub Actions for CI.

If you want a pure test automation role, this is not it. If you want to be the person who defines what good means on a team building with coding agents, and who has the credibility to hold that line in review, keep reading.

What you will own

Quality and evals, about 70 percent of the work

• Eval harnesses, golden datasets, and scoring for production agentic workflows.
• Regression gates in CI. Nothing merges without an eval verdict.
• The Playwright web and Maestro mobile end-to-end suites. Fast, and honest about failures.
• Engineering standards: code review practice, architectural patterns, targeted refactors.
• Production instrumentation for AI features, and the loop that turns real failures into new eval cases.

Product craft and resilience, about 30 percent of the work

• Code review across the Node, React, and React Native codebases. Not rubber stamps. Specific feedback that leaves the author better than you found them.
• Coaching on resilient design: error handling, retries and idempotency, failure isolation, and the tests that prove the behavior.
• Quality designed into features up front. You are in feature design early, not reviewing at the end.
• Light hands-on contribution to production code, enough to keep your review credibility and your context current.
• Game day. When something breaks on a Saturday, you are in the troubleshooting: reading traces, reproducing the failure, and driving it down.

Requirements

• 5+ years building and operating production web software.
• Strong JavaScript and TypeScript. You know the runtime, not just the framework.
• Node and React in production. React Native shipped to the app stores is a strong plus.
• PostgreSQL: schema design for read patterns, index design, query plans, migration under load.
• AWS in production: ECS or Lambda, Secrets Manager, CloudWatch, IAM. You can read the bill.
• Playwright or equivalent, used on a suite you personally had to keep green.
• CI/CD work in GitHub Actions, including making a slow pipeline fast.
• A track record of raising code quality: reviews, architectural patterns, refactors that landed.
• You have reviewed and mentored other engineers on production code. You can name specific practices that spread across a team because of you.
• Strong asynchronous written communication.

AI engineering and evaluation

A hard requirement, not a preference. Expect detailed questions.

• Daily work with frontier models and agentic coding tools such as Claude Code, Codex, or Cursor.
• Evaluations you built yourself for an LLM or agent system. What you scored, how you scored it, and what you did when the score moved.
• Dataset construction. You can explain how you sourced cases, handled labeling, and kept the set from going stale.
• Judgment about failure modes. You can name the ways an agentic workflow breaks that a unit test will never catch.
• A repeatable method for giving agents context: instruction files, tool definitions, MCP servers, subagent roles.
• Cost awareness. You track token spend and wall-clock time per unit of shipped work.

Nice to have: eval platforms such as LangSmith, Braintrust, or Langfuse; agent frameworks and SDKs; internal tooling and MCP servers; mobile end-to-end testing; multi-tenant SaaS; sports, scheduling, or field-services domains

We are looking for a Senior MLOps Engineer to join Sigma Software and help build a production-grade ML platform for a large-scale AdTech ecosystem. You will work on infrastructure powering predictive decision-making systems that process hundreds of millions of auction requests daily.

As part of a dedicated engineering team, you will contribute to scalable ML orchestration, model lifecycle automation, observability, and real-time optimization workflows. This role is ideal for engineers with strong production experience who enjoy solving complex platform and operational challenges.

We at Sigma Software offer the opportunity to work on cutting-edge ML infrastructure projects, collaborate with experienced engineers, and influence architecture decisions in a long-term strategic engagement.

CUSTOMER

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a high-load ad exchange platform handling hundreds of millions of auction requests every day and is investing in advanced predictive decision-making capabilities to improve advertiser outcomes and real-time optimization processes.

PROJECT

Sigma Software is building a predictive modeling and optimization platform integrated with a live ad exchange environment. The solution enables real-time supply scoring and filtering, audience look-alike generation, contextual performance estimation, and multi-objective optimization under operational constraints.

The project combines large-scale ML infrastructure, automated model lifecycle management, multi-tenant architecture, and advanced observability practices. The team focuses on delivering reliable, reproducible, and scalable ML systems ready for long-term Customer ownership.

Key Technologies: Python, Kubernetes, Docker, GCP, Vertex AI, MLflow, Airflow, Kubeflow, Argo Workflows, Terraform

Job Description

• Build and maintain ML training orchestration pipelines across hourly, daily, and weekly schedules
• Implement retries, backfills, and idempotent execution mechanisms
• Design and support model registry workflows including versioning, lineage, evaluation gates, and promotion processes
• Develop isolated per-advertiser model environments with namespace and configuration separation
• Build scalable refresh pipelines and publishing workflows for serving infrastructure
• Implement shadow mode and champion/challenger deployment strategies
• Develop monitoring and alerting for ML-specific metrics including feature drift, prediction drift, train/serve skew, and calibration decay
• Ensure reproducibility of ML workflows using containerized environments, pinned dependencies, and data snapshots
• Monitor training and scoring costs across tenants
• Collaborate with DevOps and SRE engineers on CI/CD and infrastructure automation
• Prepare operational documentation and platform handover materials

Qualifications

• 5+ years of experience in MLOps, ML platform engineering, or infrastructure engineering supporting production ML systems
• Strong Python skills and experience building platform-level tooling and automation
• Hands-on experience with Kubernetes and Docker
• Experience building CI/CD pipelines for ML workloads
• Hands-on production experience with MLflow, Kubeflow, Airflow, Argo Workflows, Vertex Pipelines, or similar orchestration and ML lifecycle platforms
• Experience with ML platforms and model lifecycle tools such as Vertex AI, MLflow, or Kubeflow
• Strong understanding of ML observability including drift detection, train/serve skew monitoring, and incident response
• Experience designing or supporting multi-tenant ML systems and isolated model environments
• Experience working with cloud platforms, preferably GCP
• Experience with infrastructure-as-code tools such as Terraform
• Experience with Linux environments
• Understanding of the ML lifecycle and productionization processes
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience with feature stores and feature consistency management
• Experience with large-scale batch scoring systems operating under freshness SLAs
• Familiarity with experiment tracking platforms and evaluation gates
• Experience with on-premises Kubernetes or bare-metal Linux infrastructure
• Knowledge of DVC, lakeFS, or other data versioning tools
• Experience with Bigtable, Redis, Aerospike, or similar low-latency serving databases
• GPU scheduling and training cost optimization experience
• Familiarity with SOC 2, ISO 27001, or GDPR-related compliance requirements

PERSONAL PROFILE

• Strong ownership mindset and focus on operational reliability
• Ability to work independently in complex distributed systems environments
• Strong collaboration and communication skills
• Analytical thinking with attention to scalability and maintainability
• Comfortable working in fast-paced product-oriented environments

Backend Software Engineer (Python, AI)
 

Requirements:
- 6+ years of experience in backend design and development of production-grade applications.
- Strong experience with Python
- Solid experience with databases, preferably SQL
- Experience working with large-scale systems
- Experience with AI orchestration frameworks such as LangChain or LlamaIndex, or experience integrating third-party AI APIs such as OpenAI or Anthropic.
- Knowledge of networking infrastructure.
- Cybersecurity experience - a strong advantage.
- Ability to learn quickly and adapt to new technologies.
- Strong teamwork and collaboration skills.
 

Responsibilities:
- Take full ownership of features, from product design through deployment, including end-to-end feature architecture.
- Plan, design, and develop systems with complex performance and scalability requirements.
- Design, maintain, and contribute to the company’s code infrastructure.
- Design, build, and maintain robust, scalable server-side architecture.
- Integrate with Large Language Models (LLMs) and vector

B2B, remote, US Eastern Time clients, 2 calls per week with the team.

About the Role

We are looking for a Senior Forward Deployed Engineer who combines strong hands-on engineering, enterprise infrastructure expertise, and executive presence.

You will work directly with US-based mid-market and enterprise clients, understand complex business problems, design the technical solution, build and deploy it, integrate it into existing enterprise environments, and remain the trusted technical point of contact.

This is not a ticket-based engineering role. You will own the full journey:

Business Problem → Architecture → Build → Integration → Deployment → Client Outcome
 

What You'll Do

• Own client projects end-to-end with minimal handholding
• Turn ambiguous business problems into technical solutions
• Design and build production AI agents, applications, automations, integrations, and data pipelines
• Work with complex enterprise infrastructure, ERP systems, data warehouses, APIs, and cloud environments
• Make architecture and infrastructure decisions independently
• Use AI coding tools to build and ship quickly
• Work directly with client IT teams and senior stakeholders
• Lead client meetings and clearly explain technical decisions, progress, risks, and next steps
   

What We're Looking For

Strong Engineering & Architecture

• Approximately 7–12+ years of software engineering experience; flexible for exceptional candidates
• Strong hands-on development experience with TypeScript/JavaScript and Python
• Experience with technologies such as Next.js, PostgreSQL, Supabase, and APIs
• Ability to independently design system architecture and ship production software
• Former CTOs, Tech Leads, architects, founders, and consultants are welcome if they are still hands-on
   

Enterprise Infrastructure

Strong enterprise infrastructure experience is a key requirement.

You should have experience with:

• AWS, Azure and/or GCP
• ERP systems and enterprise integrations
• Data warehouses and data pipelines
• Microsoft/enterprise environments
• Authentication, security, and APIs
• Complex or legacy enterprise systems

You should be able to confidently discuss architecture and infrastructure with a client's IT team. This was explicitly highlighted by the hiring manager as increasingly important.
 

AI-Native Engineering

You should already use tools such as Claude Code, Codex, Cursor, or similar agentic coding tools as part of your normal development workflow.

Production experience with several of the following is expected:

LLM APIs · AI Agents · RAG · Vector Databases · MCP · Agent Frameworks · AI Automations

We care especially about how you work with AI—context, documentation, agents/sub-agents, code review, testing, and QA.

Executive Presence

This is a highly client-facing position.
 

You must have:

• Excellent spoken and written English
• Strong confidence and executive presence
• Experience communicating with senior client stakeholders
• Ability to explain complex technical topics in business language
• Confidence leading meetings with executives, operations teams, and enterprise IT
• Ability to become a trusted technical advisor rather than relying on someone else to manage the client relationship

The client specifically wants engineers who can independently represent the technical work in senior stakeholder meetings.
 

You're a Strong Fit If

You are a hands-on senior engineer who thinks like an architect, operates like an owner, and communicates like a consultant.

You don't wait for detailed specifications. You can understand the business problem, decide what should be built, build it, integrate it into a complex enterprise environment, and confidently present the result to the client.

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client and the role:
We are seeking a Senior/Lead NLP & LLM Expert Consultant (AI System Architect / Consultant) to provide expert assessment and help shape the technical vision for AI-driven projects. The role will focus on evaluating existing and potential AI solutions, developing a Statement of Work (SOW), and potentially continuing to support projects as an AI System Architect / Consultant.

The ideal candidate has a strong track record of successfully designing and implementing production-ready solutions based on NLP, LLM, RAG, AI agents, and modern AI architectures.

Responsibilities:
- Provide expert assessment of AI/ML solutions and technical approaches.
- Design and advise on AI system architectures based on NLP, LLM, RAG, and AI agents.
- Help define technical approaches, architecture, and scope of work for AI projects.
- Develop a Statement of Work (SOW) based on business and technical requirements.
- Advise on the selection and integration of appropriate AI/ML technologies and approaches.
- Potentially participate in further project delivery as an AI System Architect / Consultant.
- Collaborate with technical and business stakeholders to identify optimal AI solutions.

Requirements:
- Senior / Lead-level expertise in AI, Machine Learning, NLP, and LLM technologies.
- Proven experience designing and successfully implementing solutions based on NLP, LLM, and RAG.
- Proven experience designing and successfully implementing AI agent-based solutions.
- Experience working with Large Multimodal Models (LMMs).
- Strong experience developing and implementing solutions using classical Machine Learning / Data Science approaches.
- Strong ability to evaluate AI technologies and translate business needs into practical technical solutions.

Nice-to-Have Skills/Experience:
- Expertise in knowledge bases and knowledge management systems.
- Experience with secure AI architectures and deploying models in isolated environments, including on-premise or private cloud.
- Experience with DevOps / MLOps practices and infrastructure.
- Experience working in an AI architecture, consulting, or technical advisory role.

Work format: Remote / Hybrid (Kyiv)
Engagement: Part-time / Contract
Location: Anywhere
Start: ASAP

Розглядаємо як фултайм (голова + руки), так і парттайм, де руки вам дадуть і від вас тільки голова.
__________

Хелоу, люба спільното \0/

Ми продовжуємо нарощувати команду американського Survey SaaS, що аналізує поведінку юзерів під час digital опитувань. По суті, це платформа, де бізнес може засетапити своє опитування та отримати advanced behavioural analytics від нашої моделі на підставі того, як юзер водив мишою, відповідав на різні питання, тупив та чухався.

Вам сподобається, якщо:
- в вас ідеальний інгліш. Це найважливіший фактор. Треба багато діскутувати на складні теми. Якщо вам здається, що в вас не супер сильний інгліш – го не гаяти часу.
- в вас достатньо терпіння для щоденної роботи із гіперактивним фаундером, в якого багато ідей, іноді протилежних.
- ви вмієте поєднувати грацію науки та експеріменти на колінці (напр.: взяти наукову роботу про кореляцію відповідей із часом на роздуми та придумати як превірити це за нишими даними не за мільйон доларів та часу).

По суті, нам потрібен Scientific Lead, який буду допомагати фаундеру народжувати проект, покриваючи наукову та ML частину самостійно, або за допомогою нашого ML Engineer.

На цьому я дозволю перемкнутись на англійську, бо далі конкретніше.

What you'll do

• Act as the scientific and ML lead next to the founder, turning a stream of ideas (sometimes conflicting ones) into testable hypotheses and priorities
• Design lean experiments: take a research paper (e.g. how thinking time correlates with answer quality) and find a cheap, fast way to test it on our data
• Set tasks for our ML Engineer and review the work, or build it yourself
• Own model quality: evaluation methodology, calibration, segment-level analysis, precision/recall tradeoffs
• Keep work reproducible and audit-ready (experiment tracking, clean notebooks, documented results)
   

What you need

• Deep learning on sequence/time-series data (PyTorch, CNNs built from scratch)
• Tabular ML with gradient boosting (LightGBM / XGBoost)
• Rigorous evaluation: imbalanced classification, threshold tuning, AUC-ROC, calibration, label quality
• Solid experimental design and the ability to turn research into practice
• Experiment tracking (MLflow, W&B or similar)
• Excellent spoken and written English
• The patience and structure to work day to day with a fast-moving, idea-heavy founder
   

Nice to have: behavioral/clickstream analytics, fraud/anomaly detection, survey methodology/psychometric.
 

Stack: Python, PyTorch, LightGBM, MLflow, HDF5, PostgreSQL.
 

Not an LLM/GenAI role: this is classical ML and deep learning on structured behavioral data.

The Team

• Playing Founder
• Project Manager
• ML Engineer
• 2x Fullstack Devs
• Anti-Fraud Automation QA
• DevOps

For fulltime players we offer:

• 15 paid day-offs per year.
• 10 US/UA holidays.
• Flexible schedule and fully remote work from anywhere with reliable overlap to CET.
• Senior team members.
• Minimal bureaucracy, high authonomy and trust, fast decisions.

Role Overview

We are looking for a Senior AI Engineer to own the Python AI service inside a learning-platform programme and act as the technical voice in front of the client.

About one-third to one-half of the role focuses on LLM and agent engineering. The rest covers backend development, data modelling, architecture, and the DevOps needed to keep everything shipping.

Project Description

The client is a US remote online-learning provider. Their learning-content platform is a .NET application; our team builds the Python AI service that sits alongside it, handling retrieval over the platform's content and, increasingly, agent workflows on top of it. The retrieval and recommendation layer is already delivered; the next phase is LLM engineering and agents, with custom model training a possibility later rather than a certainty.

All models used in the system are open-source and deployed on the client's own infrastructure, so this is a self-hosted inference. Agent work uses frameworks in the Google ADK / LangChain family. The .NET side is being taken over by other developers on the team, but you will still need to read and occasionally change it, which in practice means working through a coding agent in a language you may not write natively.

Two things about the environment are worth saying plainly. Requirements move constantly, so the role includes running the conversations that pin them down — meetings, diagrams, written specifications, and the patience to re-explain. And the infrastructure is branched: pipelines run in GitLab CI/CD, environments are separated, and configuration and keys live in repositories the team does not own, so working DevOps ability is a practical requirement.

Key Responsibilities

• Own the Python AI service end to end — retrieval, LLM orchestration, and the agent workflows built on top of it.
• Design and build agent workflows using a Google ADK.
• Work with open-source models deployed on the client's own infrastructure, including selection, prompting, and performance tuning.
• Own the service's data model, database design, and backend architecture.
• Run requirement sessions directly with client stakeholders and produce the diagrams and written specifications that settle them.
• Own policy-governed, auditable AI pipelines: human-review gating, content lineage, tenant/role policy enforcement. 
• Integrate the AI service with the client's .NET learning-content platform, reading and changing that code where needed.
• Maintain and extend CI/CD pipelines in GitLab across separated development environments.
• Trace configuration, keys and pipeline definitions across repositories the team does not own, and unblock delivery when they break.
• Propose an approach and defend it, rather than waiting for the requirement to arrive fully formed.
• Hand .NET-side work over to the developers taking it on, with documentation they can act on.
• Prepare the service for custom model training and evaluation if that work lands with this team.
• Owning prompt/model versioning and an evaluation harness gating changes.

Required Qualifications

• Senior-level commercial Python experience, with ownership of a production service.
• Ability to read and modify .NET / C# code.
• Hands-on LLM engineering in production — retrieval-augmented generation, context design, prompt and output handling.
• Experience building agent workflows with a framework such as Google ADK, LangChain, or LangGraph.
• Backend and database design experience, including making the architectural calls rather than following them.
• Working DevOps ability — CI/CD pipelines, containers, and multi-environment configuration.
• Willingness and ability to work in a codebase written in a language you do not primarily use.
• Strong client-facing communication: running requirement sessions, producing diagrams, and holding a position under pressure.
• English at a level that supports direct client meetings without an intermediary (at least B2 level).
• Comfort with requirements that change between meetings.

Preferred Qualifications

• Experience with document based db like MongoDB, or similar
• Experience with OpenSearch/vector search. 
• Experience with Kubernetes.
• Experience deploying and operating self-hosted open-source models.
• MLOps experience — training pipelines, fine-tuning, model monitoring and evaluation.
• Experience with learning-management or content-management platforms.
• Experience delivering alongside a partner vendor's team, including a business analyst outside your own organisation.
• Daily working experience with coding agents in a large, unfamiliar codebase.

Our client is looking for a Conversational AI Specialist.

Role Overview:We are looking for a Conversational AI Specialist to train and improve our AI agent, maintain its Knowledge Base, monitor conversation quality, and ensure responses remain accurate and up to date. You will also coordinate with our technical vendor to resolve issues and support testing and monitoring of AI updates.Key Responsibilities:

• Train and continuously improve the AI agent's responses through structured content and configuration;
• Maintain and update Knowledge Base content to keep the AI agent's answers accurate and current;
• Monitor live chat conversations to identify inaccurate or unclear AI responses and flag/fix them;
• Update AI configurations when promotions, bonuses, or policies change;
• Coordinate with our technical vendor to report and track issues, ensuring fixes are tested before going live;
• Support monitoring after updates to confirm the AI agent is working correctly for users.

Required Skills & Experience:

• To be fluent in Polish (native or near-native) and English, both written and spoken;
• Experience with AI chatbot or virtual assistant platforms (e.g., Google Dialogflow, or similar conversational AI tools) is preferred;
• Strong attention to detail and a structured, analytical mindset for identifying and resolving inconsistencies in AI responses;
• Comfortable working with basic structured data formats (e.g., reading simple JSON-like data) — full training will be provided;
• Experience using project management or ticketing tools (e.g., Jira, Trello, Asana, or similar) is a plus;
• Strong written communication skills, with the ability to document issues clearly and coordinate with technical teams.

Will be a plus:

• Prior experience in iGaming, customer support, or live chat operations is a plus;
• Prior experience in content writing, knowledge base management, or FAQ documentation is a plus;
• Curiosity about AI/chatbot technology and willingness to learn new tools.

The company guarantees you the following benefits:

• A positive workplace atmosphere that creates a culture of collaboration and support, making it a place you'll love working in;
• Competitive compensation and regular career development reviews;
• Flexible working hours and remote working options, you'll enjoy the freedom that the company provides;
• A generous vacation and sick leave policy, allowing you to take time off and enjoy a work-life balance;
• Financial assistance for professional development, helping you stay ahead of the curve and love your career path;
• Educational Allowances that give you the opportunity to expand your knowledge and experience;
• You'll have a monthly allowance for personal activities, giving you the opportunity to pursue your interests and hobbies outside of work;
• A comprehensive health insurance plan, depending on your current location;
• Referral program with financial rewards for bringing top talent to the company;
• Engaging in team-building activities and corporate parties.

Interview process:

• HR Interview with the Recruiter;
• A 1-hour technical inetrview;
• A 1-hour final interview with the team;
• Final decision.

If you find this opportunity right for you, don't hesitate to apply or get in touch with us if you have any questions!

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

We are building and scaling a mature, data-intensive B2B SaaS platform used by enterprise customers across multiple markets. The platform supports a diverse range of enterprise use cases across operational planning, information analysis, case management, and cross-team collaboration.

The product brings together large volumes of structured and unstructured data, domain-specific analytics, and configurable workflows to help users investigate complex cases, collaborate effectively, and make timely operational decisions.

About the Role:

We are looking for a Senior AI Engineer to design, build, and deliver reliable AI-powered capabilities as part of an established production platform.
You will work at the intersection of backend engineering, generative AI, and product development. Your responsibilities will cover the full delivery cycle – from exploring ideas and validating technical approaches to building production services, evaluating output quality, and improving solutions based on real-world usage.
This is a hands-on engineering role with significant ownership. You will have the opportunity to influence architectural decisions, engineering practices, and the broader adoption of AI across the product while working closely with engineering, product, and other cross-functional teams.

Key Responsibilities:

• Design, build, and deploy production-grade AI applications using LLMs, RAG, and agentic workflows.
• Build and optimize autonomous AI agents capable of planning, using tools, making decisions, and solving complex operational tasks.
• Build and operate LLM-powered product features, covering retrieval, orchestration, tool use, quality assessment, and production monitoring.
• Develop methods and tooling for assessing AI quality, diagnosing failures, monitoring behavior, and improving performance over time.
• Build scalable Python backend services and integrate AI capabilities with distributed production systems.
• Collaborate with data and product teams to productionize new AI capabilities, taking them from experimentation to reliable real-world use.
• Own technical decisions and drive AI engineering best practices and engineering standards for building, testing, releasing, and maintaining AI-enabled software.

Required Competence and Skills:

• 7+ years of software engineering experience with 1.5 years of hands-on experience building production AI applications, with proven experience delivering agentic AI systems used by real users.
• Strong software engineering background in Python or another backend language (Golang, Java, Rust, etc.), with the willingness to work primarily with Python. 
• Hands-on experience building agentic AI systems using frameworks such as LangChain, LangGraph, or similar.
• Experience deploying and operating production AI services in cloud environments, with a proven track record designing scalable, event-driven architectures using cloud messaging technologies and SQL or NoSQL databases. Experience with AWS is an advantage.
• Experience connecting AI services to existing backend systems and application interfaces.
• Experience taking AI applications from development to production and collaborating with platform engineering teams on deployment and operation.
• Experience ensuring production AI systems are reliable, secure, observable, and ready for mission-critical use, including monitoring and incident response.
• Excellent communication skills, autonomy, and accountability in ambiguous, high-velocity environments.

Nice to Have:

• Experience with React.js and full-stack development, with the ability to contribute to frontend development when needed.
• Experience with vector databases and similarity search in production AI applications.

Our client is an Israel-based technology company rebuilding a strategic military simulation and decision-support platform.

The product is designed to model modern multi-domain conflict across land, air, maritime, subsurface, cyber, information, and cognitive domains. Its technical direction combines generative AI with formal knowledge, structured world models, and explainable reasoning.

This is a full-time, long-term role. The team is being formed as the product develops, and this role will own a core part of its semantic and reasoning foundation.

The Role

We are looking for a Senior Knowledge Graph Engineer to design and build the semantic layer of the platform: ontologies, knowledge models, inference logic, and explainable reasoning.

This is not a generic graph-database role. We need someone who can apply RDF, OWL, Semantic Web principles, SPARQL, and ontology-based reasoning to a complex real-world product.

What You’ll Do

- Design the ontology and knowledge-graph architecture for a complex military-simulation domain.

- Model entities, relationships, events, constraints, rules, and operational scenarios.

- Build RDF, OWL, and SPARQL-based semantic models and queries.

- Implement ontology-based reasoning, automated inference, and explainable logical conclusions.

- Work with the AI Architect to connect structured knowledge and reasoning with LLM and generative-AI capabilities.

- Translate input from domain experts into maintainable and extensible software models.

- Build practical, production-grade semantic components rather than research-only prototypes.

- Help establish the technical foundations of a growing product team.

What You Bring

- Strong hands-on experience with knowledge graphs, ontologies, and Semantic Web technologies.

- Practical expertise with RDF, OWL, SPARQL, ontology engineering, and semantic reasoning.

- Experience with automated reasoning, inference engines, rule systems, or explainable AI.

- A track record of building real products or production systems based on knowledge graphs or semantic technologies.

- Strong understanding of knowledge representation and how to model complex, evolving domains.

- Ability to work closely with AI engineers, software architects, and domain experts.

- Experience in defence, military, intelligence, command-and-control, operational planning, or military simulation is strongly preferred.

Nice to Have

- Experience with Stardog, GraphDB, Apache Jena, Protégé, or comparable semantic technologies.

- Experience with Neo4j in a semantic, ontology, RDF, or reasoning-oriented context.

- PhD or strong research background in AI, knowledge representation, logic, Semantic Web, formal methods, mathematics, or a related field.

- Experience combining knowledge graphs and ontologies with LLMs or generative AI.

- Experience with neuro-symbolic AI or structured world models.

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Senior Software Engineer to build and operate production AI agent and search services on top of a large-scale data platform. This is hands-on work at the intersection of distributed backend engineering and applied AI: multi-agent workflows, tool-execution infrastructure, retrieval and relevance, and integrations with multiple LLM providers — all running as real, customer-facing production services with the reliability expectations that implies.

Responsibilities

• Design and implement scalable multi-agent workflows (including conversation state), tool-execution infrastructure, and data-aware tools working with catalogs, schemas, lineage, SQL, jobs, semantic metadata, data preparation, and visualizations
• Build search and retrieval capabilities with OpenSearch or equivalent — indexing, relevance tuning, hybrid keyword + vector search
• Integrate multiple LLM/model providers with secure configuration and graceful degradation
• Develop APIs and event contracts across REST, streaming/SSE, gRPC, protobuf, and SDKs, including UI integrations
• Operate, monitor, and improve production services — service health, logs, metrics, traces, search-cluster health, latency, and error rates; participate in the team's production-support model
• Investigate incidents, drive root-cause analysis, and ship durable fixes
• Design for permissions, tool approvals, multi-tenancy, and customer-data governance
• Maintain dependencies, apply security fixes, and reduce tech debt; produce designs, tests, runbooks, and documentation; lead technical discussions and review code

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field — or equivalent practical experience
• 7+ years of relevant experience, or equivalent demonstrated senior-level impact
• Strong Java software engineering skills
• Strong grasp of concurrency, data structures, API design, testing, maintainability, and backward compatibility
• Solid background in distributed systems, microservices, cloud services, or data-intensive backends
• Hands-on experience with search technologies — OpenSearch, Elasticsearch, or Lucene — including vector search, embeddings, or relevance systems
• Experience building LLM applications, agent/tool workflows, RAG systems, or model-provider integrations
• Strong production instincts: diagnosing failures via logs, metrics, and traces; designing for fault tolerance, retries, cancellation, and recovery
• SQL and familiarity with data platform concepts: catalogs, metadata, schemas, governance, lineage
• Comfort leading ambiguous, cross-service work with clear stakeholder communication and strong written documentation
• Comfortable with AI-assisted development workflows — using modern AI tools productively while critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

Desired Skills

• LangChain4j or similar agent frameworks
• Python for AI services, evaluation harnesses, or sandboxed execution
• React and TypeScript
• gRPC, protobuf, Quarkus, Kubernetes, MongoDB, KV stores, event-driven architectures
• Apache Arrow, Apache Iceberg, OpenLineage, semantic layers, or data-governance systems
• AI quality evaluation: LLM-as-judge, model benchmarking, production AI metrics
• OpenSearch operations: index lifecycle management, relevance tuning, incident response
• Open-source contributions

Details

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

We're looking for an engineer with hands-on experience building and evaluating GenAI services - from RAG and agentic reasoning systems to production-grade LLM deployments. You'll work closely with Frontend and Backend teams to bring AI agents into real products, with a strong focus on reliability, safety, and shipping working prototypes fast.

Hard Skills:

• Practical experience developing and evaluating GenAI services, including RAG systems, understanding of the "ReAct" (Reasoning + Acting) paradigm and Agentic RAG, LLM API integration, and prompt/context engineering, as well as training, fine-tuning, and deploying ML models in production environments.
• Knowledge of ML/GenAI frameworks: LangGraph or LangChain, PyTorch / TensorFlow, Hugging Face, OpenAI/Anthropic SDK.
• Practical commercial experience working with AI models via API (Gemini, Anthropic) and self-hosted models (Llama 3, Mistral, Mixtral), with an understanding of model differences based on functional/non-functional requirements (FR/NFR).
• Deep understanding of how LLMs interact with external APIs via Function Calling.
• Experience with vector databases, semantic search methods, and principles of database structuring and cleaning.
• Practical experience with at least one cloud platform (AWS, GCP, or Azure).
• Proficiency in Python and understanding of asynchronous programming.
• Understanding of the AI model lifecycle: monitoring, versioning, and quality evaluation (RAGAS, DeepEval), with hands-on experience using these tools.
• Experience with Guardrails: setting hard constraints on conversation topics and agent actions, filters that automatically strip personal data before sending requests to external LLMs, and the ability to build output filters that fact-check generated responses before they're displayed.
• Deterministic Logic Integration — running AI agents on strict schemas to prevent the model from "making things up."
• A plus: knowledge of automated testing approaches for evaluating responses across large datasets to measure hallucination rates before MVP launch.
• Knowledge of Human-in-the-loop mechanisms, ensuring agents cannot execute actions without final user verification.
• Ability to design memory systems that store context from a client's previous conversations and operations for personalization (Long-term Memory & User Context).

Soft Skills:

• Ability to clearly communicate complex technical concepts and mentor team members.
• Ability to quickly test and evaluate new libraries and approaches.
• Analytical problem solving - debugging complex "black boxes" and understanding why an agent behaves unpredictably.
• Focus on delivering a working prototype rather than a perfect research paper.
• Close collaboration with Frontend and Backend developers to seamlessly integrate AI agents into the required environment.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

About the client

Our customer is the European online car market, serving over 30 million monthly users and present in 18 countries. As part of their project, they are building an internal AI platform and are looking for a Senior AI Engineer with genuine, hands-on experience building agentic AI systems - not just integrating existing tools.

This is a hands-on engineering role for someone who has real depth in both AI system design and AWS platform engineering, and who can move fluidly between the two to build production-grade, agentic solutions.

While this is a hands-on role, you will also influence technical direction, raise the bar for engineering practices, and enable other data scientists and engineers to succeed.

Duration is 4-6 months with the possibility of prolongation. 

Time zone: EU or Canadian time zone.

We are ready to consider candidates for part-time cooperation.

Responsibilities:

• Design, build, and maintain agentic AI systems as part of the AI platform
• Work in depth with AWS to build and operate the underlying platform infrastructure
• Design and implement knowledge graph solutions to support AI reasoning and retrieval (e.g. using AWS Neptune or Neo4j)
• Collaborate with cross-functional teams to align the AI platform with business goals and internal use cases
• Troubleshoot and resolve issues across integrated systems to ensure reliable, end-to-end agentic workflows.

Requirements:

• 5+ years of experience in Software Development
• Real, hands-on experience building agentic AI systems - genuine understanding of how they work, beyond just using existing tools or frameworks off the shelf.
• Strong, in-depth AWS experience - real platform engineering depth, not incidental or side use of AWS.
• Strong Python knowledge
• Deep experience building knowledge graphs, ideally with AWS Neptune or Neo4j - especially valuable for this role.
• Experience with LLMs, prompt engineering, and AI integration patterns.
• Strong troubleshooting skills and a collaborative, team-first mindset.
• English level - upper-intermediate (both verbal and written)

Nice to Have:

• Familiarity with MCP (Model Context Protocol).
• Proficiency in infrastructure as code (e.g. CDK in TypeScript/Node.js) to manage AWS services such as Amazon Bedrock and Agent Core.
• Docker/Kubernetes familiarity.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers