Top Artificial Intelligence Jobs in Poland

Designs and delivers production-grade GenAI and agentic AI solutions for enterprise clients. Leads discovery, solution architecture, rapid prototyping, technical presales, evaluation, integration, and MVP or first-production delivery. Works with LLMs, enterprise data, workflows, orchestration, vector databases, cloud AI services, and tool-calling systems. Translates ambiguous business problems into validated solutions while addressing cost, security, compliance, observability, responsible AI, adoption, and production readiness.

Build and productionize AI solutions for customs operations, including document understanding, classification, retrieval-augmented generation, structured extraction, and multi-step agent workflows. Own evaluation, testing, monitoring, cost and latency optimization, pilot implementation, and production performance. Collaborate with operations, IT, transformation, legal, security, and technology partners while supporting privacy, security, and EU AI Act compliance.

Build and improve Restream’s web interfaces and user-facing features for the AI Clips team. Design UI and business logic, collaborate with product designers, participate in code reviews, improve system reliability, and evaluate emerging technologies. The role requires strong JavaScript and TypeScript expertise, advanced React knowledge, CSS preprocessor experience, and familiarity with backend development, cloud infrastructure, containers, and continuous deployment.

Design and deliver AI platform features and agents for a PLM application: build RAG/vector search knowledge systems, conversational AI, cloud-native deployments, CI/CD/GitOps pipelines, and ensure scalable, observable production delivery while partnering with product through discovery and iterative delivery.

Design and deliver cloud-native AI and LLM-based solutions from discovery through production. Lead presales, architecture, RAG and agentic implementations, deploy and operate ML/LLM systems, write production-grade Python, build ETL/ELT pipelines, implement MLOps/LLMOps/AgentOps, mentor engineers, and manage client technical relationships.

Design, build, and scale production AI agents and backend services for conversational reasoning, retrieval, tool-calling, and workflow automation. Integrate GDS/APIs, own architecture for distributed AI systems, improve model performance, observability, and evaluation, and define engineering best practices.

Transform business processes through AI by analyzing current workflows, designing optimized agentic solutions, gathering and documenting requirements, assessing feasibility and costs, defining human-in-the-loop workflows and evaluations, and collaborating with developers and process owners to build, test, monitor, and document production AI implementations.

Designs and deploys enterprise AI and generative AI solutions using Google Cloud, Vertex AI, BigQuery ML, Looker, and n8n. Builds data pipelines, inference workflows, retrieval layers, integrations, and AI-enabled automations using APIs and webhooks. Establishes scalable, secure, governed production practices while partnering with data engineering, BI, automation, and business teams.

Design, implement, and optimize ETL/data pipelines, data quality metrics, APIs, and enrichment for AI/ML platforms; research big-data technologies; run trials, write technical documentation, and collaborate in project teams.

Lead cloud and AI architecture: define scalable, secure system designs, technology stacks, and workflows; guide DevOps and engineering teams; promote AI/ML best practices; mentor developers and ensure delivery alignment with business goals.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Lead embedding real‑time analytics and agentic AI into resilience assurance: build continuous policy validation, predictive compliance models, autonomous AI agents, AI‑augmented audits, automated control testing, and deliver executive guidance and a 3–5 year AI assurance roadmap.

Build and operate MLOps and GenAI infrastructure: data acquisition, annotation, transformation and versioning; experiment tracking and reproducibility; scalable model training and inference platforms; automated, secure deployment pipelines with observability, versioning, rollback and multi‑tenant scaling.

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.

Own post-sale customer relationships: onboard new workspaces, provide technical support and triage, prevent churn, drive expansion, create enablement materials, and translate customer feedback into prioritized product improvements.

Lead branding and visual identity work for F&B concepts using traditional design and AI tools. Create logos, menus, signage, packaging, production-ready files, presentations, and brand guidelines while collaborating with cross-functional teams.

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.

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.

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.

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.

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

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