Top Artificial Intelligence Jobs in Krakow

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.

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.

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.

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.

Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.

Author and refine syllabus and lesson content for experienced engineers transitioning to system architecture roles. Produce AI-assisted lessons, projects, rubrics, and reviewer guides; iterate with LX designers and ensure technical accuracy and teachability. Part-time, remote, milestone-based contract (15–20 hrs/week, 4–6 months).

Teach and mentor experienced engineers transitioning to system architecture: run live sessions and office hours, review projects and run architecture review boards, provide detailed architectural feedback, record instructional videos, conduct mock interviews, and lead supporting instructor teams.

Design, develop, and maintain Java backend applications and REST APIs; integrate AI capabilities and agent-based workflows; collaborate with engineers and AI specialists to deliver scalable, production-ready solutions; contribute to CI/CD and use AI-assisted tools to improve development quality and productivity.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

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.

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.

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.

The Head of Performance will lead performance marketing strategies, focusing on optimizing paid acquisition channels and utilizing AI tools for data-driven decisions.

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

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Build and evolve shared, production-grade AI capabilities for construction software: LLM-based experiences, retrieval pipelines, agents, orchestration, evaluation-driven development, and engineering accelerators. Partner with product teams to integrate reusable patterns, apply reliability and governance guardrails, prototype new approaches, and support other engineers through collaboration, reviews, and knowledge sharing.

Drive research applying advanced ML (Transformers, GNNs, diffusion models) to semiconductor design and optimization; implement algorithms in Python/C++ using PyTorch; prototype and validate approaches, publish in top conferences, collaborate with engineers, and contribute to EDA tool integration.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Design, build, and optimize AI-driven automation workflows using LLMs, no-code/low-code platforms and custom APIs. Integrate CRMs and internal systems, develop Python/JavaScript backend logic, monitor performance, and collaborate with stakeholders to scale automated processes.

Lead and scale demand generation by building signal-driven, automated GTM systems using AI and no-code tools. Own MQL-to-pipeline metrics, optimize CAC, run performance campaigns, integrate CRM and enrichment workflows, collaborate with sales/RevOps to improve conversions and pipeline ROI.

Lead engineering to shift GTM from no-code automation to code-first, AI-agent-driven systems. Design scalable backend pipelines, integrate CRMs/APIs, build reusable agent frameworks, ensure reliability and observability, and hire/mentor a distributed engineering team to deliver production GTM infrastructure.

Remote SDR responsible for outbound prospecting, qualifying leads, booking demos, maintaining CRM records, collaborating with marketing and sales, and meeting monthly KPIs in the AI SaaS space.