Top Artificial Intelligence Jobs in Krakow

The AI Application Engineer will develop AI-enabled web applications, internal tools, and workflows, focusing on quick delivery while ensuring software quality.

Design, build, and productionize AI/ML solutions within the Workday practice. Collaborate with data scientists and engineers to deploy scalable models, implement MLOps (CI/CD, versioning, monitoring), integrate with enterprise data systems and cloud platforms, and ensure reliability and performance of AI-driven features.

Design, develop, and productionize AI/ML solutions within the Workday practice. Integrate models into production, implement MLOps (CI/CD, versioning, monitoring), use cloud AI services and container orchestration, build data pipelines, and collaborate with cross-functional teams to ensure scalable, reliable AI-driven features.

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

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.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

The Senior Solution Manager leads product strategy for AI solutions, oversees data migration initiatives, and collaborates with teams on AI architecture and product delivery.

Design, prototype, and implement scalable AI-driven automation and process-mining solutions to streamline finance shared services workflows. Identify automation opportunities, gather requirements, build and maintain automated workflows (chatbots and others), ensure compliance, monitor performance, and train end users while collaborating with business, IT, and compliance teams.

Seeking two Senior AI Engineers (EU-based, B2B, ~6 months full-time) to design and deliver production AI systems: orchestration (LangGraph/LangChain), multi-agent or conversational LLM systems, production evaluation frameworks, observability (Arize/Braintrust/LangSmith), and integrate MCP/OpenAI/Bedrock/AgentCore technologies under latency and cost constraints.

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.

Lead research and analysis of the enterprise AI services market, build market-sizing and forecasting models, publish thought leadership, advise C-level clients, present at events, support sales and peer review, mentor junior analysts, and drive research process improvements to deliver actionable insights on AI services and competitive dynamics.

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

Design and develop software solutions for AI/ML applications, propose new approaches to optimization problems, and collaborate with global teams in research and development.

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.

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

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.

Lead Full Stack Engineer owns design-to-production of an AI-first repossession and remarketing platform. Shape architecture, build scalable resilient services, integrate LLMs/GenAI for validation and document processing, drive technical decisions, reduce technical debt, operate production systems, and mentor engineers while working in cross-functional agile teams.

Support Shopify administration and Flow automations, maintain Google Apps Scripts and API integrations, manage access and SaaS subscriptions, configure Recharge subscription flows, and document processes. Use AI tools (Claude, ChatGPT) and n8n to build automations, test end-to-end, and liaise with vendors and internal teams to keep e-commerce systems running smoothly.

Founding AI Engineer responsible for end-to-end R&D and deployment of AI systems (LLMs, diffusion, vision), building rapid prototypes, orchestrating multi-model agentic workflows, integrating diverse APIs, and shipping scalable web applications to support e-commerce and future SaaS products.

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.

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.

Develop and deploy AI features for a SaaS product, architect scalable backends, optimize vector databases, and manage Azure AI infrastructure.

The Product Manager - AI will define AI product direction, collaborate with engineering, and solve complex user problems in payroll and compliance workflows.