Top Artificial Intelligence Jobs in Czechia

The Senior Quality Engineer ensures software quality by testing complex systems, developing test strategies, mentoring junior staff, and promoting quality practices throughout the organization.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Work on clinical-domain machine learning projects end-to-end: translate business questions into predictive models, implement models (Python, R, PySpark), support deployment/MLOps, collaborate with stakeholders, and contribute to team initiatives and seminars.

Execute Source-to-Contract activities: manage sourcing events, negotiate and draft contracts, maintain supplier relationships, perform data analysis and forecasting, drive process improvements and automation/AI initiatives, ensure compliance, and collaborate with legal, suppliers and business partners while mentoring junior specialists.

As an AI Legal Specialist, you will implement AI solutions, provide product expertise, and ensure seamless AI integration for legal clients.

Design, develop, test, deploy, and maintain backend services and data pipelines. Ensure technical and product excellence, performance, and engineering best practices. Collaborate across global teams, drive projects, and contribute to large-scale system design and distributed computing challenges.

Lead data science initiatives to develop AI-driven solutions for healthcare. Collaborate across teams to transform data into actionable insights and manage resources effectively.

Design, build, and integrate AI-powered features into 8am products. Collaborate with product, engineering, and data teams to prepare data, develop and deploy LLM-based services, write Python code, and build reliable, scalable AI infrastructure and monitoring.

The role involves designing and deploying AI systems for clinical operations, developing models for decision-making, and ensuring AI implementation aligns with data governance standards. Responsibilities include collaborating across teams, enhancing operational efficiency, and advancing AI applications in a regulated environment.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead and manage the commercial analytics team for Animal Health, partnering with AI/DS and Commercial IT to deliver analytics solutions, align strategy, remove barriers, demonstrate measurable business value, develop talent, and foster an enterprise analytics community.

The VP of Business Development will drive commercial growth in Europe, focusing on strategic opportunities with renewable energy stakeholders and building relationships to enhance market presence.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

Build end-to-end AI features and prototypes across backend and frontend, prototype AI agent use cases, create UI/blueprints/examples to make AI workflows usable, and ship features quickly in collaboration with product and backend teams.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Evaluate and rank personalized LLM responses using multi-turn prompts based on your personal data sources. Design prompts, assess grounding, integration, and helpfulness, perform side-by-side comparisons, write clear rationales with turn references, extract and verify debug info, and maintain strict data hygiene. Provide constructive feedback and detailed annotations while working independently as part of a global evaluation team.

Lead design, automation, and operation of Red Hat AI and OpenShift platforms. Build scalable infrastructure, automate tooling, implement monitoring, run on-call rotations, troubleshoot high-severity incidents, mentor peers, and develop Kubernetes operators and other SRE software.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Develop, document, test, and deploy AI systems and backend services. Collaborate with data scientists and engineers, contribute to DS/AI strategy, and implement automated testing, monitoring, and CI/CD practices.

Develop, deploy, and maintain AI systems including RAG solutions and language models. Write production-quality Python code, implement CI/CD and automated testing, create REST APIs, document models, present results, and collaborate in Agile teams to iterate on retrieval and generative AI methods.

Assess client support operations, analyze ticketing workflows and KPIs, design AI-driven automation (chatbots, NLP, auto-triage, agent assist), evaluate platforms (Zendesk, Freshdesk, Intercom), create AI readiness assessments and roadmaps, and deliver actionable, executive-ready recommendations.

Post-sales, customer-facing role that drives developer adoption of AI tooling by designing and running hackathons, Customer Developer Days, and repeatable facilitation infrastructure. Partner with deployment and success teams to diagnose adoption stalls, run high-touch interventions, capture outcomes, and evangelize results. Must be technically credible with developers and able to troubleshoot and demonstrate AI coding tools in real time.

Build and maintain production-grade AI systems: design and implement backend systems, integrate LLMs, vector DBs, and retrieval/RAG pipelines, deploy AI agents, contribute to CI/CD, testing, observability, and optimize performance and reliability while collaborating with product and engineering teams.