Top Artificial Intelligence Jobs in Czechia

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Embed with client teams to deploy, scale, and operationalize enterprise AI platforms and agentic systems. Own end-to-end production outcomes, design secure governed AI architectures, rapidly prototype to production, and build reusable patterns and playbooks to transfer capability to clients.

Design, build, and productionize multi-layered agentic AI systems (routers, tool-calling agents, RAG pipelines, evaluation, AI monitoring). Implement Python microservices on Kubernetes, optimize for scalability, performance, and security, and collaborate with cross-functional teams to transition AI solutions into production.

Lead end-to-end design and delivery of production-grade LLM/agent systems embedded in enterprise products. Architect AI orchestration, integrate foundation models via secure APIs, optimize latency/cost/reliability, and implement Responsible AI guardrails for sensitive HR and financial data. Drive experimentation, prototyping, and cross-functional product execution at scale.

Lead research and experimentation on AI/ML models (especially transformers) to detect and evaluate security incidents on terabytes of behavioral data, design production-ready solutions, mentor researchers, and collaborate with data, security operations, and engineering teams to drive AI applications across the incident lifecycle.

Design, build, and deploy production-grade AI/LLM systems and agentic workflows. Implement prompt and context engineering, RAG, vector search, and LangGraph-based agents. Develop backend APIs, data pipelines, and monitoring for performance, scalability, and cost optimization while collaborating with product, data science, and engineering teams.

Lead architecture, build, and deploy context-rich LLM and agent-based AI solutions. Design prompts, manage context/RAG and vector search, implement production Python services, integrate models and agents, ensure data pipeline quality, monitor and optimize agent performance, and guide cross-functional teams on scalable, secure AI deployments.

Design, develop, test, deploy, and maintain ML/AI software solutions. Ensure technical and product excellence, performance, and engineering best practices. Collaborate across global teams and contribute to strategic goals at an early-stage startup.

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, 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.

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 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.

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.

Own B2C customer support operations and vendor governance while designing and deploying AI copilots, prompt libraries, workflows, and no-code automations. Partner cross-functionally to identify bottlenecks, drive operational improvements, build dashboards, measure AI adoption and KPIs, and maintain documentation to enable scalable execution.

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.

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

Lead QA for AI platforms by defining test strategy, automation, and quality governance. Design AI-specific and CI/CD-integrated test frameworks, validate agent behavior, manage QA team, enforce quality gates, drive metrics and continuous improvement across engineering and 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.

Hands-on lead full-stack engineer who designs scalable architecture, ships production SaaS features, integrates AI tools, mentors senior engineers, improves deployment pipelines, and drives engineering quality.

Design, build, test, and maintain ML/AI software solutions; ensure product and technical excellence; apply ML, NLP, and large-scale system practices; collaborate across global teams; follow engineering best practices and support strategic goals.

Lead and grow an in-office AI engineering team focused on NLP and computer vision. Define AI strategy and roadmap, drive design, development, and deployment of high-performance models, collaborate cross-functionally, mentor engineers, and lead hands-on research and prototyping to deliver scalable AI solutions aligned to business KPIs. Expected to work from the Prague office.

The Machine Learning Researcher will explore novel approaches for threat detection through data analysis, designing experiments, deploying models, and collaborating across teams in a dynamic research environment.