Top Artificial Intelligence Jobs in Poland

Hands-on platform engineering role building and operating an internal Agentic AI platform: implement infrastructure-as-code, security and secrets management, GitOps delivery, observability, autoscaling, API/AI gateway routing, and run day-to-day operations including incident response, backups, and runbooks.

Lead and deliver AI-driven projects, POCs, MVPs and pilots across Business Assurance IT; coordinate cross-functional teams, manage budgets, timelines and governance, and support AI roadmap and cross-pillar prioritization.

As an AI Full-Stack Engineer, you will design user-facing AI applications, develop frontend interfaces, manage backend integrations, and enable AI workflows while collaborating with teams to optimize decision-making processes for Roche's Partnering organization.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.

Design, build, and operate the AI storage layer for GPU-heavy edge and core deployments. Architect and optimize storage for distributed training and inference, integrate storage with Kubernetes (CSI), tune performance (RDMA, NVMe-oF, GPU Direct Storage), lead reliability and operational practices, and provide staff-level architectural ownership and cross-team influence while executing hands-on storage engineering.

Own design, deployment, and operation of high-performance AI GPU fabrics and datacenter networking (RoCE, RDMA, BGP/ECMP, EVPN/VXLAN). Provide staff-level architecture, cross-team technical leadership, incident escalation, automation (Python/Bash), and operational standards for global datacenter and edge deployments, including inter-datacenter transport and security.

The HR Leader will build the HR function for a team focused on AI and digital finance in Singapore, developing a comprehensive people strategy and overseeing employee lifecycle management.

The role involves leading finance and operations for a new initiative in financial services, including financial planning, compliance, and operational infrastructure establishment.

The Principal Product Designer will drive product design strategy, mentor other designers, and lead complex design initiatives while ensuring high-quality, user-centered design practices.

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.

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.

The Senior CyberSecurity AI Engineer will design, deploy, and support endpoint security solutions, leveraging AI/ML for threat detection and organizational collaboration.

Lead development and governance of a centralized catalog of reusable AI components (prompts, agents, templates, RAG patterns). Define standards, manage lifecycle (intake, tagging, publishing, deprecation), ensure compliance with infrastructure and security, collaborate with engineers to productize reusable assets, monitor usage and impact, and support enterprise AI best practices and adoption.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

As an AI Content Manager, you'll oversee content production, design workflows, ensure quality across languages, and leverage AI to enhance content output.

Lead development of an enterprise-grade AI agent governance product: strengthen platform config and CI/CD support, treat agents as deployable configuration, build discovery, inventory, policy engine, audit trails, cost attribution, partner with Atlassian, and conduct enterprise customer discovery.

Design, build, and maintain AI-powered automations, integrations, and internal tools across SaaS/cloud platforms. Use AI coding assistants, agents, and MCP services to develop scripts, APIs, serverless workflows, and dashboards to automate onboarding, lifecycle management, data synchronization, and IT processes while collaborating with cross-functional teams.

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.

Lead design and delivery of AI agent and multi-agent systems. Hands-on develop LLM-integrated agent pipelines, build CI/CD, containerization, IaC and AI observability, mentor engineers, align architecture with product goals, and contribute to pre-sale technical solutions.

Design and build reproducible, containerized cloud platform challenges covering networking, IAM, observability, deployments, scalability, and disaster recovery. Develop automated validation tests, reference and defective solutions, debug environments, and document architecture and validation logic.

Enable engineering teams to adopt AI tools by creating training, workshops, demos, reference implementations, documentation, and hands-on examples. Partner with platform, architecture, security, and engineering leadership to translate AI capabilities into developer workflows, define best practices, gather feedback, and improve internal AI developer experience.

Act as the technical bridge between AI/analytics capabilities, customers, and sales across EMEA. Identify use cases, run discovery workshops, demos, pilots and PoCs, enable sales via content and training, feed product/R&D with field insights, and support product launches. Role requires regular travel across EMEA and close collaboration with HQ.

Lead and own an AI-as-a-Service offering for developer teams: define vision and roadmap, set and track KPIs, coordinate engineering, gather and prioritize user feedback, drive adoption, onboard and train users, and engage stakeholders to evolve the service.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.