Top Artificial Intelligence Jobs in Poland

Lead the design of AI-enabled product and service experiences. Facilitate discovery, journey mapping, and user research; mentor designers; collaborate with product, engineering, and data science to create transparent, explainable, and human-centred AI solutions that drive trust and adoption.

Design, develop, and maintain Java backend applications and REST APIs; integrate AI capabilities and agent-based workflows; collaborate with engineers and AI specialists to deliver scalable, production-ready solutions; contribute to CI/CD and use AI-assisted tools to improve development quality and productivity.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Lead a Special Projects engineering team to explore and implement Generative AI and LLM solutions, design and deliver GCP/Vertex AI cloud products from development to production, write and review code (Python/Java), mentor engineers, collaborate with product and data science, and drive continuous learning and innovation.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Build and own AI/LLM-assisted features in a Ruby on Rails production environment. Integrate LLM APIs, design prompts and retrieval/context patterns, implement safeguards and fallbacks, improve reliability, latency, cost, and security, and write/maintain unit, integration, and E2E tests. Debug production issues and iteratively improve system behavior.

Build and own AI/LLM-assisted features in Ruby on Rails: integrate LLM APIs, implement retrieval/context patterns and prompts, ensure reliability, tests, fallbacks, performance, security, and production operability.

Design, build, and operate core AI systems (support, marketing, client orchestration) using LLMs and vector databases. Implement async pipelines, background jobs, and scalable production deployments. Measure performance, iterate, and improve system reliability, logging, and monitoring.

The Data and AI Product Manager will develop and execute a data product roadmap, prioritize AI use cases, gather requirements, ensure data governance, and manage stakeholders to drive data-driven decision-making in tourism.

The Head of Performance will lead performance marketing strategies, focusing on optimizing paid acquisition channels and utilizing AI tools for data-driven decisions.

The AI Data Engineer will design and implement workflows for automating data reporting, using AI to enhance the telemedicine platform's analytics.

Lead product vision, roadmap, and P&L for Data, AI and Insights products in travel. Translate market and customer needs into backlog, write user stories, support sales and delivery, guide scrum teams, analyze maintenance issues, and drive product-market fit and profitability.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

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 and deliver enterprise data management and analytics solutions for financial analytics. Build data pipelines, sourcing/scraping/OCR workflows, ML models and POCs, collaborate with engineering and product teams, and deploy solutions on cloud (AWS) using MLOps practices. Translate domain needs into platform strategy and drive implementation across teams.

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 technical strategy for AI platform development at CaptivateIQ. Architect AI systems, set quality standards, and mentor engineers while collaborating with senior leadership.

Develop AI-enabled workflows and methods to accelerate design, simulation, control, and validation of complex power-electronics systems. Apply ML (surrogate models, optimization, RL, Bayesian methods) integrated with MATLAB/Simulink, PSCAD, or PLECS to enable design-space exploration, model reduction, anomaly detection, and reliability assessment. Collaborate with local and global teams, external partners, and disseminate results via reports, patents, and publications.

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.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Develop, maintain and deploy expert systems for kiln and grinding optimization. Implement AI/ML and advanced control strategies, integrate soft sensors and process models, ensure DCS/PLC communication, provide remote support, tune MPC/Fuzzy/PID controllers, train operators, and maintain technical documentation.

Build and evolve shared, production-grade AI capabilities for construction software: LLM-based experiences, retrieval pipelines, agents, orchestration, evaluation-driven development, and engineering accelerators. Partner with product teams to integrate reusable patterns, apply reliability and governance guardrails, prototype new approaches, and support other engineers through collaboration, reviews, and knowledge sharing.

Hands-on lead full-stack engineer who designs scalable architecture, ships production SaaS features, integrates AI tools, mentors senior engineers, improves deployment pipelines, and drives engineering quality.