Top Artificial Intelligence Jobs in Poland

Lead end-to-end business operations workstreams for RDT Data & AI, translating requirements into implementation plans, performing deep-dive analyses, and improving processes (compliance, quality, dashboards, vendor management). Act as a trusted advisor to cross-functional stakeholders to drive operational excellence and organizational effectiveness.

Lead medium-to-large Data & AI initiatives, plan and execute Global Data Strategy workpackages, manage cross-stream dependencies, resources, and backlogs. Drive PMO governance, delivery of technical releases, and coordinate Metadata Management, MDM, and PBAC workstreams while partnering with data governance and mentoring engineering teams.

Drive enterprise adoption of agentic AI by identifying high-impact automation opportunities, prototyping agent-based solutions, partnering with process owners to re-engineer workflows, scaling prototypes into production, and measuring productivity and value creation while building reusable patterns and enabling an AI-native culture.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

As an AI-First Frontend Engineer, you will develop high-performance, AI-integrated user interfaces for telehealth applications, manage team workflows, and optimize web performance.

Own acquisition, onboarding, and activation for JetBrains AI. Improve product entry points, trials, demos, and onboarding flows. Lead experiments, analyze funnels and cohorts, remove friction, and partner with design, engineering, analytics, marketing, and monetization to drive activation and conversion.

Design and implement agentic tools, translating product needs to production, scale prototypes, improve semantic search, build evaluation pipelines.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead AI strategy and architecture for an AI-native data integration platform: select LLMs, design RAG/agentic systems, build transformation generation and validation features, implement model ops and monitoring, and define responsible, auditable AI practices while remaining hands-on and managing an AI engineering team.

Lead design and delivery of ML solutions for perception, prediction, and planning for assisted and automated parking. Define CV and multimodal sensor-fusion approaches, drive architecture from data pipelines through deployment, establish evaluation metrics, mentor engineers, and collaborate with global teams to improve algorithms and production performance.

Design and optimize ML models for perception, prediction, and planning in automotive parking systems. Build computer-vision and multi-sensor processing solutions, trajectory forecasting, training/evaluation pipelines, analyze large datasets, validate models in real-world scenarios, and collaborate with software, systems, and product teams to improve algorithms and tools.

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.

Build, validate, document, and productionize predictive models and scalable analytics solutions. Clean and manage large datasets, create visualizations and business-facing tools, standardize repeatable processes, and partner with BI, IT, and business stakeholders to ensure deployed models deliver ongoing value.

Lead SEO and AI search initiatives end-to-end: improve technical SEO (site structure, crawlability, indexation, Core Web Vitals, structured data), increase visibility across search and LLM-driven surfaces, run experiments and reporting, build AI-powered workflows for content and automation, collaborate with product/content/analytics, and create data-driven content to drive measurable organic growth.

Manage delivery of AI-enabled and infrastructure technology projects, coordinate cross-functional engineering teams, oversee scope, timelines, risks, margins, client communication, resource allocation, and contractual/commercial aspects.

Design, build, and maintain end-to-end AI/ML solutions including data pipelines, model development, edge deployments, and integrations. Define camera and imaging setups for computer vision use cases, coordinate installations, monitor model performance, and collaborate with IT, software, and infrastructure teams to ensure scalable, secure, and maintainable solutions.

Lead AI adoption for business teams: run assessments, prioritize use cases, design and deliver workshops, activate champions, remove blockers, track adoption KPIs, and hand teams off as self-sufficient while routing engineering needs to technical teams.

Design and build Python-based intelligent agents and multi-agent workflows using Autogen or similar frameworks. Integrate LLMs, develop cloud-native services, create async APIs, implement data ingestion and storage (SQL/NoSQL/vector DBs), ensure scalability, and contribute to orchestration, testing, documentation, and CI/CD.

Build and maintain enterprise-grade software tools within an Agile team, mentor peers, apply engineering best practices, use AI-driven development assistants, and collaborate across Product, Cloud, and Support to deliver high-quality solutions.

Lead and deliver enterprise-grade Apple platform software: design features, break down customer problems, implement end-to-end solutions, mentor teammates, apply engineering best practices, ensure quality and reliability, and collaborate across product, cloud, and support in an agile environment.

Own the AI platform roadmap and build reusable capabilities (prompting, RAG, tool calling, agents, evaluation, observability). Run discovery, prioritize AI investments, partner with Product, Engineering, Design and Data, guide other PMs, define success metrics, and ensure scalable, reliable AI-powered features that deliver customer and business value.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Lead design and delivery of AI-powered features for CLion and C++ workflows. Architect AI integrations and agent tools, prototype and validate debugging, code-understanding, refactoring, and navigation workflows, build benchmarks and evaluation frameworks, and collaborate with product, ML, QA, and engineering teams to set technical direction and roadmap for AI-assisted C++ development.

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