Top Artificial Intelligence Jobs in Czechia

Build and ship governed AI applications for departments across the organization using LLM APIs, MCP servers, and internal AI platforms. Partner with business teams, lead technical enablement sessions, advise on AI integrations, document reusable patterns, and contribute to AI infrastructure including gateways, RAG, model operations, and evaluation frameworks. Ensure implementations meet security and governance standards while supporting use-case qualification and adoption reporting.

Own the technical strategy and delivery of internal AI enablement. Build complex AI applications and lead the development of the AI platform, including LLM gateways, RAG infrastructure, model operations, evaluation frameworks, guardrails, and an internal AI marketplace. Establish engineering standards, mentor enablement engineers, provide technical education, partner with Security and Platform Engineering, and translate organizational AI goals into measurable roadmaps.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Embed with customers to prototype, build, and ship production-grade AI and agentic applications; advise on AI strategy and deployment; codify repeatable architectures and playbooks; implement LLMOps/MLOps practices (RAG, fine-tuning, semantic search, observability); mentor teams and feed product improvements.

Lead technical AI discovery with enterprise customers: run hands-on workshops, scope high-value private AI use cases, assess data and infrastructure readiness, build lightweight POCs and reference architectures, and translate validated opportunities into engineering-ready blueprints for production handoff.

Lead and resolve complex AI infrastructure and platform incidents across NVIDIA GPU, Kubernetes, networking, storage, and hardware domains. Drive reliability, observability, automation, RCA, and operational standards; mentor engineers; collaborate with engineering, vendors, and datacenter teams to improve platform performance, capacity, and resilience. Support adoption of AI-powered operational services and evaluate emerging technologies to enhance platform operations.

Own and evolve the enterprise data platform and architecture (lakehouse, ingestion, governance, security). Optimize throughput, cost, and observability; enable AI consumption via governed data products; lead design reviews, mentor engineers, and prototype platform components.

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

Architect and lead development of an AI Media Production platform: build and deploy reproducible generative image/video pipelines, train LoRAs end-to-end, integrate control techniques (ControlNet, IP-Adapter, inpainting, reference conditioning), implement automated QC against product references, create one-click internal tools for non-technical teams, and own the technical roadmap with the Design Director.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build production-grade AI and automation systems to integrate ad platforms, CRM and analytics for Performance Marketing. Ensure data accuracy by reviewing SQL and data models, create monitoring/diagnostics and human-in-the-loop decision workflows, and roll the solution out across nine markets.

Join a cross-functional team to design and implement AI agents and LLM-powered applications. Use GitHub Copilot, Python, and prompt engineering to build agent workflows, integrate tools, and leverage technical documentation as knowledge sources.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Lead design and implementation of CI/CD and AI-related product features, owning end-to-end delivery. Build and secure CI/CD systems, work with containers, registries, and Kubernetes, extend agentic AI into customer experiences, guide architecture, and collaborate closely with Product and front-end teams.

Architect, develop, and deploy AI-enabled solutions using React frontends and Python or .NET backends. Enhance LLM engines with Azure OpenAI, sync data to Azure AI Search, use Kafka for messaging, build robust APIs, and mentor junior developers while collaborating with product, DevOps, and QA teams.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

This role involves designing, developing, and deploying AI and automation solutions, ensuring integration with enterprise systems, and meeting security standards. The position requires hands-on engineering skills in AI development, particularly with Python and AI frameworks.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.