Top Artificial Intelligence Jobs in Poland

Build end-to-end AI features and prototypes across backend and frontend, prototype AI agent use cases, create UI/blueprints/examples to make AI workflows usable, and ship features quickly in collaboration with product and backend teams.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

The role involves designing and scaling MLOps infrastructure on AWS for autonomous driving systems, requiring expertise in machine learning and cloud architecture.

Review and validate neuroscience research, case studies, and AI-generated content; create clear explanations, annotations, and scenario-based exercises; identify errors and inconsistencies; apply current neuroscience methods and provide structured, evidence-based feedback to improve content quality for AI training.

Review biochemical datasets, protocols, and literature to evaluate AI-generated content for scientific accuracy. Develop realistic scenarios and training materials, identify errors or missing context, provide structured, evidence-based feedback, and explain complex biochemical concepts clearly. Participate in iterative content review and validation to improve AI understanding of life science topics.

Review and validate pharmacology content, create realistic cases and structured datasets, evaluate AI-generated pharmacology responses, identify errors and missing context, provide evidence-based feedback, and translate complex pharmacological concepts into clear training materials while documenting methodology and collaborating with multidisciplinary teams.

Lead and scale an AI-native engineering organization that orchestrates humans and AI coding agents across multiple fast-moving product ventures. Hire and mentor PMs and engineers, set engineering standards (CI, security, evals), make architecture and build-vs-buy-agent decisions, stay hands-on shipping code and risky spikes, and ensure commercial and product outcomes across the portfolio.

Lead AI governance for Global Procurement, defining frameworks, ethics, compliance, and value measurement. Align AI portfolio with procurement priorities, engage stakeholders, embed governance across IT, Legal, and Analytics, drive adoption and measurable ROI while overseeing performance, risk, and strategic fit of AI solutions.

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.

Lead short-term AI adoption program: build internal AI champions, create usage policies and training, support teams implementing AI tools, contribute to internal AI tool builds, and ensure responsible, secure deployment across distributed teams.

Support productionising AI/ML solutions for Workday products under senior guidance. Collaborate with data scientists and engineers to deploy, monitor, and maintain models using cloud, containerisation, and MLOps practices while following responsible AI and data privacy guidelines.

Lead design and delivery of backend-focused, high-scale digital banking features across the full SDLC. Architect microservices, integrate APIs, mentor engineers, improve CI/CD and system resilience, and build LLM-based assistants and personalization using OpenAI and ML models while ensuring security and scalability.

Own delivery from spec to production using AI coding agents. Define verification, review and accept agent-generated code, tune agent toolchain, step in to hand-code (mainly Go), and ensure spec->code->test traceability.

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.

Design, build, and maintain production-grade GenAI helpdesk automation using N8N and AWS. Integrate LLMs and Codex into workflows, implement infrastructure automation, CI/CD, and observability, and troubleshoot end-to-end automation flows involving Jira and Slack while collaborating with Support, IT, and Engineering.

Design, build and deploy low-code/no-code automation and AI solutions (Power Platform, Copilot Studio, AI Builder) for Merchant Acquiring Operations. Lead discovery, prototyping, UAT and production rollouts, ensure governance and compliance, deliver training, mentor citizen developers, measure adoption and refine solutions, and inform the automation roadmap.

Operate and support production AI infrastructure (NVIDIA GPUs, Kubernetes, high-performance networking). Monitor, troubleshoot, and resolve infrastructure, networking, hardware, and platform incidents; participate in incident response and root cause analysis; improve monitoring, observability, automation, and operational processes; maintain runbooks and documentation; collaborate with engineering, vendors, datacenter and service teams. Work shift-based operations.

Lead end-to-end implementations of QVAC-powered AI solutions, acting as primary client contact, defining roadmaps, coordinating cross-functional teams, managing risks and governance, and ensuring production readiness of privacy-preserving, edge, and distributed inference deployments.

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.

Embed with product teams to design, implement and deploy production-grade AI/LLM solutions. Drive AI adoption, agentic workflows, RAG and prompt engineering, build scalable ML/LLM pipelines, ensure MLOps, observability, security and business impact.

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.

Design, build, and deploy agentic AI solutions and workflow orchestration to automate complex business processes. Rapidly prototype PoCs with LLM frameworks, integrate agents with enterprise systems, define governance and guardrails, and upskill internal teams through training and documentation. Collaborate cross-functionally to embed automation into product and operations and ensure responsible, secure deployments.

Lead design, deployment, and scaling of ML infrastructure and production models. Mentor engineers, perform code reviews, architect real-time and batch inference pipelines, integrate new ML technologies, ensure model governance and explainability, collaborate with Data Science, and run A/B testing and monitoring to align models with business KPIs.

Design, build, and deploy production-grade AI agents and agentic workflows by orchestrating LLMs, retrieval systems, and tools; integrate solutions into client applications, optimize agent performance, ensure observability and LLMOps best practices, and collaborate with engineering and data teams.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern lakehouse/AI platform. Improve data modelling, quality, reconciliation, performance and operational reliability, partner with stakeholders, and provide technical leadership for delivery.