Top Artificial Intelligence Jobs in Poland

Design and implement full-stack applications and AI agents while mentoring engineers and leading project ownership, focusing on rapid development and deployment of AI capabilities.

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.

Develop and evaluate machine learning and multimodal AI models for precision oncology using clinical, imaging, pathology, molecular, genomic, and outcomes data. Build and curate datasets, perform statistical and survival analyses, feature engineering, model validation, and create visualizations and reports. Collaborate with clinicians, engineers, and informaticians to translate analytics into actionable clinical and research insights.

Design and build production-grade AI applications and services in Python, integrating LLMs with backend APIs and enterprise systems, implementing evaluation frameworks, observability, CI/CD, and reusable components to ensure reliable, testable, and scalable AI features.

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.

As an AI/ML Engineer, you will develop and optimize AI solutions for renewable energy assets, collaborating with a skilled team to deploy algorithms in real-world projects.

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.

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.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Lead full-cycle recruitment for AI/ML roles across Europe and LATAM: build pipelines, partner with hiring managers, screen candidates, manage interviews and offers, and improve AI-focused hiring processes.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Lead end-to-end Data & AI engagements: define objectives, shape architecture, manage delivery and teams, stay hands-on in implementation, and help grow accounts and pre-sales.

Design, build, and deploy production-grade AI workflows and intelligent agents that automate cross-functional business processes. Translate stakeholder needs into scalable integrations, track adoption and outcomes, run experiments, and iterate to deliver measurable efficiency gains across Sales, Finance, Support, and Operations.

Design and lead end-to-end AWS architectures for enterprise AI/GenAI projects, run discovery and Well-Architected reviews, define IaC standards, advise on data and security architecture, support presales, and mentor engineering teams to deliver production-grade AI solutions.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

Lead architecture and delivery for Fortives HR technology ecosystem, owning Oracle HCM, Boomi integrations, ServiceNow HRSD, Eightfold and LMS platforms. Define API-first integration patterns, implement AI/automation use cases, ensure data governance and compliance, and guide integration engineers while partnering with HR and IT stakeholders to modernize HR systems.

The Senior AI Product Manager will lead cross-functional teams to define, develop, and manage B2B SaaS products, focusing on customer value and market needs, while utilizing Agile methodologies and Generative AI technologies.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.

Develop and maintain backend applications for Consumer Finance, design and implement new backend features, build and integrate AI-driven solutions (RAG, vector DBs, LLMs, agents), collaborate with business and technical teams, and ensure quality, performance, and security.

Lead AI adoption across engineering platforms by translating AI strategy into implementable plans, defining use cases, driving PoCs, enabling governance, and collaborating with architects, data and security teams to modernize systems and improve engineering productivity.

As a Lead/Staff Full Stack Engineer, you'll design and implement AI platforms, mentor engineers, and drive full-stack application development and deployment in a remote-first environment.