Top Artificial Intelligence Jobs in Poland

You will modernise legacy services using AI, implement automation in the development lifecycle, and demonstrate AI-first practices within a team of senior engineers.

As an AI Solution Architect, you'll lead AI strategies, design architectures, evaluate frameworks, ensure compliance, and collaborate with teams for production deployment in enterprises.

Oversee product strategy and development in EdTech and AI, manage teams, and drive data-informed decisions aligned with business goals.

Develop and maintain automation testing frameworks, execute tests, and implement AI-driven quality practices while collaborating with product teams.

As a Staff Software Engineer at Redpanda, you will design and develop the Agentic Data Plane, lead technical projects, and enhance operational excellence in cloud services, focusing on reliability and scalability.

Design and build services for Redpanda's Agentic Data Plane, collaborating globally and ensuring high reliability and scalability of AI systems.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

The role involves defining AI data architecture, managing data quality, and ensuring security while guiding teams on best practices for data governance and AI integration.

The role focuses on modernizing enterprise data architecture for AI readiness, involving data governance, quality, security, and orchestration using Airflow. Responsibilities include defining standards, establishing data contracts, ensuring data quality, and providing mentorship.

The AI Data Engineer will design and implement workflows for automating data reporting, using AI to enhance the telemedicine platform's analytics.

Build and own AI/LLM-assisted features in a Ruby on Rails production environment. Integrate LLM APIs, design prompts and retrieval/context patterns, implement safeguards and fallbacks, improve reliability, latency, cost, and security, and write/maintain unit, integration, and E2E tests. Debug production issues and iteratively improve system behavior.

Design, build, and operate core AI systems (support, marketing, client orchestration) using LLMs and vector databases. Implement async pipelines, background jobs, and scalable production deployments. Measure performance, iterate, and improve system reliability, logging, and monitoring.

Build and own AI/LLM-assisted features in Ruby on Rails: integrate LLM APIs, implement retrieval/context patterns and prompts, ensure reliability, tests, fallbacks, performance, security, and production operability.

As a Staff/Senior AI Engineer, you will develop production-ready coding agents and improve AI model capabilities for JetBrains' software development tools. Collaborating with teams, you'll focus on model training, evaluation, and product integration to enhance user experience.

The Software Engineer will shape AI governance in developer tooling, build product features, collaborate with teams, support users, and innovate rapidly.

Lead end-to-end delivery of AI, ML, Big Data, and software projects for international enterprise clients. Act as primary client contact, define scope, manage risks, budgets, and resources, mentor cross-functional teams, drive business-aligned technical roadmaps, support pre-sales, and contribute to account growth and recruitment.

The Senior Product Manager will lead AI-focused product strategies for internal and external developer experiences, oversee product lifecycle management, and mentor junior team members while leveraging data and AI technologies to enhance developer productivity and satisfaction.

Lead product vision, roadmap, and P&L for Data, AI and Insights products in travel. Translate market and customer needs into backlog, write user stories, support sales and delivery, guide scrum teams, analyze maintenance issues, and drive product-market fit and profitability.

Lead product vision and strategy for the Mosaic Data Intelligence portfolio. Manage P&L, roadmap, investments, and priorities. Capture customer and market needs, translate into user stories, work with engineering and commercial teams to deliver AI/data solutions, support sales and stakeholder engagement, triage maintenance, and conduct demos.

Lead technical strategy for AI platform development at CaptivateIQ. Architect AI systems, set quality standards, and mentor engineers while collaborating with senior leadership.

Lead development and operationalisation of Responsible AI frameworks and compliance processes. Translate regulations (EU AI Act, GDPR) into controls, implement risk assessment, bias detection, model validation, embed governance in development lifecycles, manage audits and incident response, and drive stakeholder engagement and training.

Lead and manage a team of engineers building services for Redpanda's Agentic Data Plane, ensuring operational excellence, career development, and effective roadmap delivery.

Lead and mentor a team of senior full-stack developers to design, build, and ship scalable full-stack features and internal tools using Next.js, React, TypeScript, Prisma, and PostgreSQL. Architect systems, optimize performance, integrate third-party APIs, establish engineering best practices, and own projects end-to-end while leveraging AI-assisted development tools.

Design, administer, and govern Microsoft Power Platform and AI-driven automation across development, validation, and production. Build Power Apps, Power Automate flows, Copilot Studio agents, integrations with enterprise systems (Dataverse, Microsoft 365, ServiceNow, Jira, APIs), maintain GxP-compliant platform lifecycle and ALM, enable CoE governance, monitor platform health, and drive adoption through training, templates, and operational excellence.

The Data and AI Product Manager will develop and execute a data product roadmap, prioritize AI use cases, gather requirements, ensure data governance, and manage stakeholders to drive data-driven decision-making in tourism.