Top Artificial Intelligence Jobs in Poland

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.

The AI/NLP/Data Engineer will develop ML and AI models, enhance NLP systems, and build supporting infrastructure while collaborating with teams on deployment and documentation.

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 and maintain an AI pipeline that generates structured Markdown from a large legacy codebase using self-hosted LLMs, vector search, and graph queries. Integrate RAG components (Qdrant, Neo4j), add NLI validation, develop prompts/templates, and collaborate with data engineers and QA to meet strict KPIs for coverage and factual precision.

Drive research applying advanced ML (Transformers, GNNs, diffusion models) to semiconductor design and optimization; implement algorithms in Python/C++ using PyTorch; prototype and validate approaches, publish in top conferences, collaborate with engineers, and contribute to EDA tool integration.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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.

Own the vision, roadmap and delivery of AI products: translate business problems into product requirements, define KPIs and business cases, drive discovery-to-deployment, validate outcomes, promote adoption, and scale reusable AI solutions across markets while ensuring compliance and governance.

Lead and execute AI-first integrated marketing strategies and campaigns to drive acquisition, retention, product launches, community growth, and conversion optimization. Use AI across research, copy, personalization, automation, analytics, and reporting. Collaborate cross-functionally to build funnels, scale education and partnerships, run webinars and evergreen campaigns, and continuously experiment to improve ROI and customer journeys.

Lead design and scaling of AI-first operational systems across the company. Improve processes, documentation, reporting, automation, and cross-functional execution while partnering with executive leadership to increase efficiency, KPI visibility, and AI adoption.

Manage end-to-end global recruitment using AI to source, screen, interview, and onboard candidates. Maintain ATS workflows, recruitment documentation, KPIs, and interview coordination. Partner with leadership to improve hiring quality, reduce time-to-hire, enhance candidate experience, and build scalable, AI-first people operations and SOPs.

Design, build, test, document, deploy, and improve AI-first business automations across CRM, HR, Sales, Marketing, Customer Success, and Operations using no-code/low-code platforms, APIs, webhooks, and AI agents to increase efficiency, accuracy, scalability, and user experience.

Lead UX and UI strategy for AI-first products across web and mobile. Own end-to-end design lifecycle: research, wireframes, prototypes, testing, high-fidelity design, developer handoff, and post-launch optimization. Use AI tools to accelerate research, ideation, prototyping, accessibility reviews, and design systems. Partner with Product, Engineering, AI, Marketing, and Leadership to improve adoption, engagement, retention, and accessibility while raising design standards and shaping product strategy.

Use AI-augmented workflows to create marketing campaigns, websites, videos, social content, presentations, and digital assets. Maintain brand quality, prototype and iterate with emerging AI tools, collaborate across teams, and manage multiple projects to improve engagement, conversions, and creative systems.

Partner with executives to identify, prioritize, and implement AI opportunities across the organization. Develop AI roadmaps, evaluate technologies and vendors, redesign workflows, increase automation maturity, measure outcomes, establish AI governance, and drive business transformation to improve customer experience, productivity, and decision-making.

Lead visual design across product, marketing, education, and business assets using strategic systems thinking and AI-assisted tools. Create brand identity, presentations, reports, landing pages, social assets, and production-ready collateral while ensuring consistency, quality, accessibility, and cross-functional collaboration to improve user understanding and engagement.