Top Artificial Intelligence Jobs in Poland

Design, build, evaluate, and deploy LLM-driven features and AI systems. Collaborate with engineering and product teams to implement RAG, monitoring, CI/CD, and scalable cloud ML deployments while continuously improving model performance.

As the AI Agents Solutions Architect at Kraken, you'll design and build an AI-native finance operating system, automating finance operations and ensuring compliance in a regulated environment.

The AI Video Editor / Motion Designer creates AI-driven videos to enhance advertising effectiveness. Responsibilities include video production, concept development, utilizing AI tools, performance analysis, and collaboration with team members.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

Build and scale core systems powering GenAI customer agents. Design reliable, high-performance distributed services, work across Java/Scala/Python stacks, use AI dev tools to accelerate engineering, ensure strong tests and observability, collaborate on architecture and product decisions, and deliver customer-focused solutions.

As an AI Creative Producer, you'll develop and scale AI-generated content across various platforms, collaborate with teams, and enhance production workflows.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Build and maintain enterprise web applications using .NET and Angular, participate in full SDLC, integrate AI services and APIs, leverage AI-powered development tools and LLM/RAG solutions, contribute to architecture and technical decisions, collaborate with clients and delivery teams, and ensure compliance with enterprise/regulatory standards.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Design, build, and maintain Claude-powered AI agents and modular multi-agent systems to automate bookkeeping and accounting workflows. Integrate with accounting platforms (Xero, QuickBooks, MYOB, etc.), implement human-in-the-loop processes, confidence scoring, audit trails, and continuous learning mechanisms, and collaborate with leadership to prototype and scale AI-driven accounting platform capabilities.

Senior technical leader defining architecture and delivery of AI/GenAI and data platforms across supply chain and enterprise digital. Build and operate lakehouse, model lifecycle, CI/CD, model serving, vector search, governance, and compliant validated releases. Mentor engineers, set engineering standards, integrate SAP and enterprise data, and ensure security, GxP compliance, observability, and cost-efficient production solutions.

Lead enterprise-wide Data & AI architecture for cross-domain initiatives, define target architectures and digital guardrails, govern AI adoption, advise senior leadership on technology trade-offs, and ensure reusable, scalable AI and data products across Pharma and Diagnostics.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Design, build, and deploy AI agents and automations for Infrastructure & Operations using Microsoft Copilot Studio, Power Automate, Azure AI and scripting. Identify automation opportunities across I&O pillars, develop self-healing operations, create reusable AI patterns and enable engineers through training. Measure automation impact and ensure compliance with AI governance, security, and regulated-industry requirements.

The role requires reviewing and evaluating AI-generated outputs and original content while applying local insights and maintaining quality standards. Strong analytical skills and attention to detail are necessary.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

As a Principal Forward Deployed Engineer, you'll deploy JetBrains Central in enterprise environments, implement AI governance, mentor teams, and advise customers on AI-native development best practices.

The AI Engineer will develop and maintain multi-agent AI systems for performance media trading, focusing on data integration, quality assurance, and production reliability.

The AI Data Engineer will build and enhance data pipelines for AI initiatives, ensuring compliance, governance, and collaboration with cross-functional teams.

Review, evaluate, and annotate AI-generated text, images, audio, and video for accuracy and guideline compliance. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve datasets and annotation workflows.

Lead two Gen AI teams to design, build, and deploy next-generation conversational agents. Manage roadmaps, mentor engineers, coordinate with product and business stakeholders, and integrate advanced LLM techniques and external APIs into production systems.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Lead end-to-end development and integration of AI features (LLMs/ML) into web/mobile apps and APIs. Design AI-first UX, craft and optimize system prompts, build agentic workflows (LangChain/CrewAI/AutoGen), implement error-handling and hybrid cloud/on-prem deployments, and collaborate within product squads using Agile/Scrum.

Design and implement CI/CD and IaC for LLMs, automate model and dataset versioning, provision GPU/TPU infrastructure, enable safe experiment gates and progressive delivery, and build monitoring/observability and feedback loops for production LLM endpoints.

Lead design, build, and operate a multi-tenant AI platform and LLMOps infrastructure across cloud and on-prem. Own vector DBs, feature stores, model hosting (Kubernetes + GPU orchestration), and developer self-service tooling. Standardize LLM-as-a-service offerings and enable squads with APIs, templates, and documentation.