Top Artificial Intelligence Jobs in Warsaw

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

Integrate and deploy LLMs/Generative AI into web applications by building APIs, backend Python systems, data pipelines, and scalable microservices. Use containerization (Docker, Kubernetes), cloud platforms, vector databases, and CI/CD to deliver secure, high-performance AI-powered features.

Lead end-to-end adoption of AI/LLM solutions across the credit risk model lifecycle: refine use cases, build PoCs with Python/SAS/SQL, tune existing LLMs, ensure traceability and regulatory compliance, and drive scalable, auditable adoption across COO Risk while influencing senior stakeholders.

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

Design, build, and operate backend services for an AI-driven financial assistant. Own full SDLC, ensure performance, scale, resilience, and collaborate in small autonomous teams.

Build and ship native Android features in Kotlin for an AI-enabled neobank. Collaborate with product and design to create clean user journeys, integrate backend APIs, improve performance and reliability, use analytics and production data to iterate, and implement on-device AI where it improves the UX.

Design, build and ship native iOS features for BJAK's AI Neobank app using Swift. Implement insurance, payments and financial flows; integrate REST APIs; optimize performance, crashes, memory and battery; use analytics and production feedback; collaborate with product and design to create clean mobile user journeys and AI-assisted experiences where helpful.

Build and ship native Android features for an AI-enabled neobank using Kotlin. Design clean mobile flows for payments, insurance, claims and financial services, integrate backend APIs, improve performance and reliability, use analytics to iterate, and implement on-device AI where it improves the user experience.

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.

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 design and implementation of AI-native features for PyCharm and Python tooling. Architect integrations (MCP, agents), prototype and ship features, guide engineering decisions, evaluate AI tools/providers, and expose IntelliJ Platform capabilities to AI workflows while collaborating with product, ML, QA, and senior developers.

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.

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, develop, and maintain automated test suites for web apps, APIs, and AI/LLM features. Evaluate LLM outputs for accuracy, relevance, and hallucinations; create prompt tests using Promptfoo; build Playwright-based UI automation; write reusable Python scripts and Postman collections; integrate tests with CI/CD; collaborate with product and engineering in Agile ceremonies.

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.

The Principal AI Engineer will solve high-impact problems using AI, develop end-to-end solutions, and influence technical direction across teams.

Support integration of AI and automation across the data platform, build ETL and data quality checks using Dataiku, prepare data for reporting and analysis, improve reporting via ML and automation, collaborate with analysts and stakeholders to deliver reliable AI/data solutions and identify data improvement opportunities.

Lead product ownership for Data, Analytics & AI, driving roadmap, stakeholder alignment, and telephony/AI migrations. Deliver conversational AI and VUI solutions, oversee model lifecycle, testing, and integrations (Genesys, Salesforce, Snowflake) while influencing adoption across decentralized global markets.

The Principal Test Automation Engineer will design and implement testing strategies, apply AI tools for quality, enforce testing standards, and integrate testing into CI/CD processes.

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.