Top Artificial Intelligence Jobs in Warsaw

Lead architecture and hands-on development of an AI-native underwriting platform. Design scalable backend services in Python, integrate with React/TypeScript frontends, promote engineering standards, implement observability and security, and use AI-assisted development tools to improve productivity while mentoring the team and driving the platform from MVP to production.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Join the MLOps team to deploy, maintain, monitor, and optimize machine learning models in production. Build data and model pipelines, implement CI/CD for ML, ensure model performance and cost efficiency, and collaborate cross-functionally to deliver scalable AI solutions.

Lead design, build and deploy advanced AI systems and LLM-driven applications (RAG, agents). Architect scalable ML/LLM pipelines, apply MLOps/LLMOps, integrate with enterprise systems, ensure security and compliance, mentor teams, and act as technical SME in presales and delivery.

Senior Director analyst providing thought leadership and actionable research on infrastructure and operations AI strategy. Produce insights, presentations, and client advisories; guide AI infrastructure, cloud adoption, integration, governance, and vendor selection. Lead research agenda, mentor analysts, collaborate cross-functionally (infrastructure, engineering, enterprise architecture, cybersecurity), support sales, and represent Gartner at events. Drive process improvements and influence senior stakeholders to align infrastructure and operations with business outcomes.

Design polished mobile-first and web interfaces for BJAK's AI and financial products. Turn requirements into wireframes, flows, prototypes and high-fidelity UI; maintain the design system; collaborate with PMs and engineers for pixel-perfect implementation; iterate from user feedback and analytics while ensuring accessibility, responsiveness and consistency across platforms.

Chatdesk Experts provide support for e-commerce brands through social media and email, working remotely and setting their own schedules.

Lead end-to-end delivery of AI, ML, Big Data, and software projects for international enterprise clients. Act as primary client contact, define scope, manage risks, budgets, and resources, mentor cross-functional teams, drive business-aligned technical roadmaps, support pre-sales, and contribute to account growth and recruitment.

As a Staff/Senior AI Engineer, you will develop production-ready coding agents and improve AI model capabilities for JetBrains' software development tools. Collaborating with teams, you'll focus on model training, evaluation, and product integration to enhance user experience.

Design, administer, and govern Microsoft Power Platform and AI-driven automation across development, validation, and production. Build Power Apps, Power Automate flows, Copilot Studio agents, integrations with enterprise systems (Dataverse, Microsoft 365, ServiceNow, Jira, APIs), maintain GxP-compliant platform lifecycle and ALM, enable CoE governance, monitor platform health, and drive adoption through training, templates, and operational excellence.

Lead vendor governance and managed-service optimization for RDT Data & AI. Define KPIs/SLOs, drive AIOps and automation adoption, establish governance and operating models, analyze operational trends, and implement continuous improvement to reduce toil and costs.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Build and productionize AI/ML solutions for bank risk management, including fine-tuning LLMs, architecting RAG systems, processing large unstructured data, designing agentic workflows, extracting NLP signals for credit/risk features, and ensuring models are testable, reproducible and deployed via Azure DevOps on GCP.

As an Applied AI Engineer, you will architect, build, and deploy enterprise-grade AI solutions, work with customers to address complex challenges, and collaborate with cross-functional teams to innovate using AI tools.

Build end-to-end Generative AI solutions (chatbots, voicebots, Talk-to-Data) including data ingestion, retrieval/RAG pipelines, orchestration, model selection/fine-tuning, MCP integrations, evaluation, deployment, and support pre-sales and delivery activities.

Lead AI DevOps Engineer to architect, deploy, and automate scalable, secure AI/ML/GenAI solutions. Responsibilities include model serving, CI/CD, IaC, cloud (Azure) infrastructure, Kubernetes, monitoring, cost optimization, stakeholder advising, mentoring engineers, and coordinating cross-functional teams to deliver production-ready GenAI applications.

Lead and scale multidisciplinary AI engineering teams to define AI strategy and deliver AI-native products. Build backend platforms and agent-based LLM solutions, establish evaluation, observability, AI-focused CI/CD, tooling, and orchestration, and partner with product and business stakeholders to drive delivery and innovation.

Participate in a 14-day notification-sorting pilot using a qualifying Google Pixel device. Interact naturally with notification-generating apps, complete daily sorting tasks and short feedback/check-in forms, report issues, and follow project instructions. Work is remote, short daily sessions (10–15 minutes), managed via Upwork.

Build end-to-end AI-native product features across frontend, backend, and model integrations. Design and deploy agent workflows, streaming real-time interactions, reliability and fallback mechanisms, and integrate LLMs, memory, and external tools to complete multi-step tasks. Collaborate with ML, backend, and product teams and iterate based on real usage and failure modes.

Build and ship end-to-end AI features: design prompts, tools, memory and agent workflows; convert model outputs into reliable behavior; debug model-to-product stack; optimize latency, cost, and reliability; create lightweight evaluation frameworks and collaborate with product and engineering to deliver production ML systems.

Build production-grade generative AI applications using private LLMs: design prompts, implement RAG/vector search, integrate LLMs into backend services and web UIs, ensure enterprise security/compliance, perform data analysis, and collaborate on end-to-end deployments and automation.

The Senior Software Engineer will design and implement AI capabilities, architecture, and product experiences, focusing on production AI features and collaborating with cross-functional teams.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Build ShipMonk's AI domain: consult with stakeholders to identify real problems, design and implement AI/ML projects from prototype to production, make AI tools accessible to non-technical teams, and balance technical complexity with business impact.