Top Artificial Intelligence Jobs in Poland

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.

Design, architect, and deploy end-to-end AI solutions (RAG pipelines, autonomous agents) to improve risk management. Translate regulatory and capital requirements into technical specifications and prompts, build infrastructure and testing strategies, and collaborate with regulators, senior risk managers, and engineering teams on distributed AI systems.

Design, build, test and support batch and streaming data pipelines and curated datasets on a Lakehouse/AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Work with stakeholders to deliver production-ready, well-tested, high-performance data products and support operational monitoring and reconciliation.

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.

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.

As a Senior Embedded AI Engineer, you'll integrate Large Language Models into robotics, optimize AI models for embedded platforms, and work on simulations with real robots.

Post-sales, customer-facing role that drives developer adoption of AI tooling by designing and running hackathons, Customer Developer Days, and repeatable facilitation infrastructure. Partner with deployment and success teams to diagnose adoption stalls, run high-touch interventions, capture outcomes, and evangelize results. Must be technically credible with developers and able to troubleshoot and demonstrate AI coding tools in real time.

Design and implement full-stack applications and AI agents. Collaborate with teams to deliver customer-focused solutions while ensuring secure and reliable development practices.

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.

Translate legal expertise into AI-powered workflows by designing, configuring, and testing Libra Assistants. Partner with clients to map processes, run pilots, enable adoption, and provide product feedback to improve prompt quality and accuracy. Create learning materials and drive high customer satisfaction while working cross-functionally with Product, Engineering, Sales, and Customer Success.

The Commercial AI Solutions Analyst will collaborate with teams to implement AI initiatives and ensure data quality for business insights. Responsibilities include data validation, user acceptance testing, and promoting AI literacy within sales.

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

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.

Evaluate AI coding agents end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide opinionated feedback, distinguish response quality levels, and help define standards for great AI-developer interactions. Participate in take-home evaluation and interview as part of a rolling, project-based engagement.

Identify, prioritize, prototype, and implement LLM- and AI-driven business use cases using no/low-code and automation tools. Measure impact on business metrics, enable independent AI use across functions, build an internal AI community, and monitor/integrate market AI solutions.

Design and introduce NVIDIA AI solutions for telecommunications (network management, cloud/edge/NFV), lead technical aspects of complex deployments and PoCs, create trainings and technical content, and drive customer adoption and integration of AI platforms.