Top Artificial Intelligence Jobs in Czechia

Perform AI research and evaluations, build feasibility studies and POCs, develop benchmarks and toolkits, collaborate on AI-driven drug design, scan literature, support IP creation, mentor colleagues, and enable model deployment using Python and cloud services.

Lead architectural strategy and hands-on implementation of scalable, AWS-native platforms and Agentic AI orchestration. Mentor 30+ developers, build POCs, enforce design governance, ensure reliability and security, and integrate complex data and AI workflows across global teams.

Design and scale end-to-end AI/ML solution patterns, data pipelines, and model architectures; lead pilot MVPs/PoCs into production; define AI governance and best practices; collaborate with architects, engineering, and sales to drive services growth.

Design, build, and deploy production LLM systems and workflows for search, analytics, reasoning, and automation. Lead fine-tuning, training pipelines, model optimization, deployment, monitoring, and continuous improvement while translating business needs into scalable LLM architectures.

Design, build, and maintain scalable, secure cloud infrastructure across AWS and Azure using Terraform, Docker, and CI/CD. Manage production environments, monitoring, security, IAM, secrets management, automation, cost optimization, and collaborate with dev/DevOps teams to ensure high availability and reliability.

Design, build, and operate production-grade knowledge graph and graph+LLM systems for large-scale unstructured data. Own data pipelines, entity/ontology modeling, graph and vector database deployment, embedding generation, and ML/LLM integration. Optimize inference for latency, cost, and reliability, and drive architecture and MLOps for production AI solutions.

Part-time AI Knowledge & Adoption Intern who builds and maintains an internal AI knowledge library, creates clear playbooks, coordinates workshops and KTs, liaises between engineers and business teams, and promotes responsible, ethical AI adoption to increase organizational use and confidence.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Design and implement CI/CD and IaC for LLMs, automate model and dataset versioning, provision GPU/TPU infrastructure, enable safe experiment gates and progressive delivery, and build monitoring/observability and feedback loops for production LLM endpoints.

Lead AI solution rollout and adoption across Europe by coordinating headquarters and local teams, delivering training, tracking adoption and performance, analyzing feedback, and driving continuous improvement to maximize business impact.

Lead deployment and adoption of AI solutions across European subsidiaries. Gather and prioritize business requirements, coordinate regional testing and feedback, liaise between global and regional teams, manage project governance, risks, and timelines, and adapt AI solutions to local market, regulatory, and business needs.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Project-based work speaking with AI chatbots in Czech (cs_CZ) using voice mode, pursuing predefined conversation goals, then rating the interactions in English. Work is remote, uses personal smartphone and laptop, follows provided guidelines and confidentiality, and may require varied recording environments.

Post-sales technical lead for strategic EMEA accounts, advising on cloud-native and AI deployments. Own technical relationships, troubleshoot escalations, design architectures for LLM/agentic workloads, drive product adoption, deliver training, and feed customer insights to Product and Engineering.

Develop, maintain, and run automated tests (end-to-end, unit, integration) for AI/LLM applications using Playwright and Pytest. Debug pipeline failures, improve test coverage, collaborate with developers, and use AI to support QA automation and test instruction generation.

Build ShipMonk's AI domain: consult with stakeholders to identify real problems, design and implement AI/ML projects from prototype to production, make AI tools accessible to non-technical teams, and balance technical complexity with business impact.

Own global go-to-market strategy and positioning for SUSE's AI portfolio. Translate technical cloud-native, containerized, and edge inferencing capabilities into executive-level messaging, create high-value content, enable sales and partners, lead ecosystem marketing, and provide market and competitive intelligence to influence product and GTM decisions.

The AI-First Graphic Designer will create visual assets for digital platforms, maintain brand consistency, and collaborate on marketing campaigns. The role requires managing multiple design requests in a fast-paced environment.

Develop tools and libraries in Python for neural network optimization, contribute to open-source projects, and enable AI technologies on devices.

As the AI Search Innovation Strategist, you'll lead the evolution of SEO practices, focusing on AI visibility performance and collaborating across teams to optimize architecture for AI integration.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The AI & Automation Manager will lead AI and automation transformations, setting vision, overseeing delivery of AI products, coaching teams, and proving business impact across international markets.

Build and maintain real-time streaming data pipelines and feature stores to power low-latency model inference. Collaborate with ML and AI infra teams on data contracts, observability, and SLAs; drive batch-to-real-time migrations and evaluate emerging streaming/feature store technologies.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

As a Junior AI Platform Engineer, you'll support development of cloud-based AI infrastructure, improve CI/CD processes, and collaborate with a cross-functional team to deploy AI models.