Top Artificial Intelligence Jobs in Poland

Lead execution of the AI transformation strategy: prototype and scale AI-powered services, own revenue for innovation offerings, build repeatable piloting frameworks, implement AI agents internally, and partner cross-functionally to drive commercial impact and operational efficiencies.

Lead execution of the AI transformation strategy, prototype and scale AI-powered services, own revenue targets for innovation offerings, build operational frameworks and governance, and drive internal AI adoption to improve efficiency and margins while partnering cross-functionally with Sales, Product, and Delivery.

Lead architecture and delivery for Fortives HR technology ecosystem, owning Oracle HCM, Boomi integrations, ServiceNow HRSD, Eightfold and LMS platforms. Define API-first integration patterns, implement AI/automation use cases, ensure data governance and compliance, and guide integration engineers while partnering with HR and IT stakeholders to modernize HR systems.

Own the end-to-end AI-powered content process: develop prompts, generate and edit SEO-friendly articles, fact-check, optimize for search, repurpose content, maintain editorial guidelines, and publish via CMS while improving AI workflows and collaborating with writers and SEO specialists.

Lead design, build, and deploy production LLM applications and AI agents on cloud infrastructure. Own end-to-end ML pipelines, integrate AI with ERPs/CRMs/data warehouses, implement monitoring and optimization, and lead the development team while collaborating with stakeholders to translate business needs into AI solutions.

Lead a remote team of senior full-stack engineers to design, build, and ship scalable Next.js/React/TypeScript applications. Architect systems, design schemas, optimize DB performance with Prisma/PostgreSQL, integrate third-party APIs (Stripe, Supabase), adopt AI-assisted tooling like Cursor IDE, own delivery end-to-end, and mentor developers to enforce best practices.

Develop AI-driven applications focusing on full stack web development, utilizing AI frameworks for search and retrieval, enhanced user experiences, and ensuring security and compliance.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Develop and maintain backend applications for consumer finance, design and implement new functionalities, build and integrate AI-driven solutions (RAG, vector DBs, LLMs, multi-agent systems), collaborate with business and technical teams, and ensure quality, performance, and security.

Develop and maintain backend applications for Consumer Finance, design and implement new backend features, build and integrate AI-driven solutions (RAG, vector DBs, LLMs, agents), collaborate with business and technical teams, and ensure quality, performance, and security.

Design and lead end-to-end AWS architectures for enterprise AI/GenAI projects, run discovery and Well-Architected reviews, define IaC standards, advise on data and security architecture, support presales, and mentor engineering teams to deliver production-grade AI solutions.

As an AI Engineer, you will implement AI solutions using Azure OpenAI API, develop machine learning models, and collaborate with teams across industries including retail and healthcare.

Lead the development of AI-native test automation processes and frameworks, ensuring technical quality and operational efficiency in AI-augmented testing.

The Principal AI-Augmented Test Automation Engineer will lead AI-native test automation, define processes, establish frameworks, and collaborate with stakeholders on quality strategy and operational efficiency.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

As a Lead/Staff Full Stack Engineer, you'll design and implement AI platforms, mentor engineers, and drive full-stack application development and deployment in a remote-first environment.

Lead a distributed full-stack engineering team to design, build, and deliver production-grade applications. Architect systems, design schemas, optimize performance, integrate third-party APIs (Stripe, Supabase, music platforms), and develop admin dashboards. Drive end-to-end delivery, establish best practices for code quality, testing, documentation, deployment, and mentor developers while using AI-assisted tools like Cursor IDE to accelerate development.

Design and implement backend business logic and orchestration for AI coding agents, integrate agents with product surfaces, external APIs and CLIs, build highly concurrent asynchronous workflows, improve context handling and failure recovery, collaborate with product and ML teams, and own technical design and code quality.

Lead strategy, governance, and scaling of enterprise prompting and Agentic AI capabilities. Define prompting frameworks, standards, QA, and best-practice playbooks. Partner with testing, BAU, and training to validate prompts, embed solutions into operations, and drive adoption. Report prompting maturity to executives and manage risks to ensure AI moves from pilots to measurable, scaled business outcomes.

Design and deliver enterprise AI and Generative AI solutions, build data pipelines, integrate AI outputs, and define AI delivery best practices.

Design, build, and optimize scalable model-serving infrastructure for generative and classical AI models. Integrate serving with AI frameworks and applications, drive performance and reliability in distributed, high-concurrency environments, and develop agentic solutions and generative algorithms to enable automated intelligent systems.

Own and evolve ShiftKey's AI infrastructure: organizational knowledge platform, retrieval and indexing (semantic RAG + re-ranking + HyDE), agentic cloud infrastructure on AWS, graph-based retrieval design, platform APIs, reference agent implementations, and AI cost/governance and observability for production-scale regulated (HIPAA) workflows.

Design, build, and operate scalable GCP cloud infrastructure for AI/ML platforms using Terraform and IaC. Manage multi-project environments, security, governance, cost control, hybrid networking, GPU provisioning, and self-hosted autoscaling CI/CD runners. Integrate infrastructure tooling into CI/CD pipelines and take end-to-end ownership of delivered solutions while collaborating with stakeholders.

Design, build, and operate an ML platform on GCP (Vertex AI). Develop and automate ML pipelines, provision infra with Terraform, create CI/CD workflows with GitHub Actions, build production Python APIs, implement model monitoring and observability, and support GPU-based training in HPC/Slurm environments.