Top Artificial Intelligence Jobs in Warsaw

As a Technical Product Manager, you will shape product vision for AI capabilities, collaborate with engineering and design teams, manage product lifecycle, and drive prioritization to deliver scalable solutions while communicating across technical and non-technical audiences.

Design and implement responsive React+TypeScript frontends for GenAI products. Build conversational UIs, streaming interfaces, agent dashboards, and data visualizations. Ensure UX quality, accessibility, frontend security, and integrate with backend APIs, LLM services, and streaming endpoints while collaborating with cross-functional teams.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

The Senior CyberSecurity AI Engineer will design, deploy, and support endpoint security solutions, leveraging AI/ML for threat detection and organizational collaboration.

The Data Scientist will develop AI and machine learning solutions for operational efficiency and decision-making, collaborate with teams, and communicate insights to stakeholders.

Lead embedding real‑time analytics and agentic AI into resilience assurance: build continuous policy validation, predictive compliance models, autonomous AI agents, AI‑augmented audits, automated control testing, and deliver executive guidance and a 3–5 year AI assurance roadmap.

Develop and maintain complex Ruby on Rails backend services for Workato's Workbot chatbot platform. Contribute across the development lifecycle, research Slack and MS Teams ecosystems, write testable code, propose architectural improvements, and support continuous product and platform enhancements.

Lead development and operationalization of enterprise AI security strategy, standards, and guardrails. Perform risk assessments and security reviews of AI applications, agentic workflows, and traditional domains (code, cloud, networking, APIs, identity). Identify systemic gaps, recommend remediations, and provide technical leadership to influence secure architecture and adoption at scale.

Lead development and governance of a centralized catalog of reusable AI components (prompts, agents, templates, RAG patterns). Define standards, manage lifecycle (intake, tagging, publishing, deprecation), ensure compliance with infrastructure and security, collaborate with engineers to productize reusable assets, monitor usage and impact, and support enterprise AI best practices and adoption.

Create and evaluate realistic B2B sales scenarios for AI training, covering prospecting, discovery, qualification, solution selling, proposals, negotiation, forecasting, and deal progression. Assess account plans, buyer engagement, pipeline quality, deal risks, and commercial strategies involving complex enterprise stakeholders. Apply established sales methodologies and evaluation rubrics, refine work based on feedback, and support calibration and quality reviews. This is a short-term remote contractor assignment of up to nine weeks.

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.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Lead and develop a high-performing team focused on AI enablement capabilities, enhancing internal engineering processes, and aligning product development with organizational goals.

Design, develop, and deliver enterprise-grade Generative AI and ML platform services, reusable frameworks, APIs, and agentic applications. Build scalable inference, deployment, monitoring, and CI/CD capabilities, implement event-driven streaming solutions, collaborate with architects and data scientists, and ensure security, governance, scalability, and operational excellence for AI/ML lifecycles.

Design, build, and deploy production-grade AI agents and agentic workflows by orchestrating LLMs, retrieval systems, and tools; integrate solutions into client applications, optimize agent performance, ensure observability and LLMOps best practices, and collaborate with engineering and data teams.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern lakehouse/AI platform. Improve data modelling, quality, reconciliation, performance and operational reliability, partner with stakeholders, and provide technical leadership for delivery.

Design, architect, and deploy end-to-end AI solutions (RAG pipelines, autonomous agents) to improve risk management. Translate regulatory and capital requirements into technical specifications and prompts, build infrastructure and testing strategies, and collaborate with regulators, senior risk managers, and engineering teams on distributed AI systems.

Design, build, test and support batch and streaming data pipelines and curated datasets on a Lakehouse/AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Work with stakeholders to deliver production-ready, well-tested, high-performance data products and support operational monitoring and reconciliation.

Lead end-to-end implementations of QVAC-powered AI solutions, acting as primary client contact, defining roadmaps, coordinating cross-functional teams, managing risks and governance, and ensuring production readiness of privacy-preserving, edge, and distributed inference deployments.

Design and implement full-stack applications and AI agents. Collaborate with teams to deliver customer-focused solutions while ensuring secure and reliable development practices.

Design and implement full-stack applications and AI agents while mentoring engineers and leading project ownership, focusing on rapid development and deployment of AI capabilities.

Translate legal expertise into AI-powered workflows by designing, configuring, and testing Libra Assistants. Partner with clients to map processes, run pilots, enable adoption, and provide product feedback to improve prompt quality and accuracy. Create learning materials and drive high customer satisfaction while working cross-functionally with Product, Engineering, Sales, and Customer Success.

Design, build, and maintain production-grade GenAI helpdesk automation using N8N and AWS. Integrate LLMs and Codex into workflows, implement infrastructure automation, CI/CD, and observability, and troubleshoot end-to-end automation flows involving Jira and Slack while collaborating with Support, IT, and Engineering.

Design and implement AI applications, scalable solution architectures, and enterprise GenAI systems. Develop Python software, integrate LLMs and vector databases, and implement APIs, microservices, containers, CI/CD, monitoring, and security controls. Collaborate with users, participate in implementations, document solutions, review and test code, debug applications, and solve complex problems using systemic and first-principles thinking.

Design and implement scalable AI applications and high-performance architectures using Python and generative AI. Develop and evaluate LLM and RAG solutions, deploy containerized systems, and integrate APIs, microservices, cloud platforms, monitoring, and security controls. Participate in documentation, testing, debugging, deployments, and client presentations while resolving user issues. Support junior developers and contribute to resilient, event-driven enterprise systems handling large datasets.