Top Artificial Intelligence Jobs in Czechia

Conducts EMEA procurement payment operations while meeting service levels and policy, tax, and legal requirements. Collaborates with procurement and business teams, improves payment processes and user experience, supports automation and AI initiatives, and builds strong stakeholder relationships. The role also assists with strategy, continuous improvement, and project or process implementation.

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.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Lead AI Value Analyst responsible for building and maintaining business cases for AI initiatives, estimating and tracking benefits, and reporting ROI. Partners with stakeholders, Finance, and AI CoE to quantify productivity, cost, revenue, and risk impacts, prioritize opportunities, validate data and assumptions, and produce dashboards and presentations to communicate realized value and governance across the AI portfolio.

Design, build, and operate an enterprise AI Hub providing shared runtime, CI/CD, model registry, observability, governance, and developer tooling to enable scalable, secure production AI and self-service capabilities across cloud and hybrid environments.

Design, build, deploy, and optimize production-grade generative AI agents, copilots, and intelligent workflows. Lead end-to-end delivery from solution design and prompt engineering to deployment, monitoring, and continuous improvement. Partner with business stakeholders to transform processes for AI-first execution, define KPIs, ensure governance and security, provide training and change management, and contribute reusable frameworks and best practices across the AI Center of Excellence.

Design, implement, test, and maintain full‑stack software across backend and frontend. Participate in full product lifecycle, review peer code, create documentation, collaborate globally, and help drive strategic goals.

Design, build and maintain backend services and data pipelines; drive technical design and implementation; ensure product and technical excellence through testing, performance tuning, instrumentation, and automation; collaborate across global teams and lead projects in distributed, large-scale systems.

Lead discovery and assessment of AI and transformation opportunities: run workshops, analyze processes, define problem statements, quantify value, build business cases, align stakeholders, prioritize use cases, and transition validated opportunities to delivery teams.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Ensure reliable, secure, and compliant IT services by managing IT service lifecycle, user access and identity integrations, automating IT general controls, supporting cloud security and continuity (Azure/AWS), coordinating vendors and development teams, and implementing automation and AI-driven improvements aligned with ITIL and regulatory requirements.

Build and deploy production-grade LLM capabilities for the Kayzen Console. Responsibilities include developing AI-backed APIs and full-stack features, implementing RAG, embeddings, agents, and tool calling, and establishing evaluation, monitoring, observability, and feedback systems. The role optimizes quality, latency, reliability, and cost; troubleshoots production issues; collaborates with Product, ML, and Engineering; and turns prototypes into maintainable AI products.

Lead design, development, and deployment of AI solutions; evaluate feasibility; build data pipelines; optimize model performance; mentor junior engineers.

Build and maintain production-grade agentic AI systems for an internal AI platform. Design AWS infrastructure, knowledge graph solutions using Neptune or Neo4j, and LLM-based reasoning and retrieval workflows. Troubleshoot integrated systems, collaborate across teams, influence technical direction, and support engineering best practices. The contract is expected to last 4–6 months, with possible extension, and requires strong Python, AWS platform engineering, and agentic AI expertise.

Leads global privacy and AI legal matters for a SaaS employment platform. Advises Product, Engineering, Security, and business teams on privacy by design, AI governance, data protection, workforce data, incidents, regulatory compliance, vendor reviews, transfers, and contract terms. Builds scalable policies, controls, templates, training, and customer-facing trust materials while monitoring global privacy and AI regulations.

Lead Usercentrics’ Office of AI across engineering platforms and company-wide business adoption. Own AI enablement, champion networks, use-case delivery, platform strategy, evaluation frameworks, governance, hiring, and team development. Guide LLM, agent, RAG, model operations, and AI infrastructure initiatives while partnering with Security, Legal, Platform Engineering, and business leaders. Deliver measurable outcomes, establish technical standards, and grow a mixed technical and non-technical organization.

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.

Operate Dataiku and Databricks environments supporting production analytics and machine-learning pipelines. Responsibilities include promoting data-science projects to production, packaging Python environments, configuring monitoring and alerting, triaging pipeline failures, administering platform users and policies, coordinating with data owners, tracking operational demand, writing runbooks, and supporting automation design reviews. The role requires strong operational judgment, Python and AWS experience, clear incident communication, and effective support for data scientists transitioning projects from development to production.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Supports security assessments and control deployments for enterprise SaaS and AI systems. Reviews configurations, identifies misconfigurations, helps develop security patterns and mitigations, evaluates AI and generative AI risks, operationalizes security controls, and monitors security tools and dashboards for compliance. Works cross-functionally while developing expertise in cloud, application, AI, and enterprise security.

Own design, deployment, and operation of high-performance AI GPU fabrics and datacenter networking (RoCE, RDMA, BGP/ECMP, EVPN/VXLAN). Provide staff-level architecture, cross-team technical leadership, incident escalation, automation (Python/Bash), and operational standards for global datacenter and edge deployments, including inter-datacenter transport and security.

Design, build, and operate the AI storage layer for GPU-heavy edge and core deployments. Architect and optimize storage for distributed training and inference, integrate storage with Kubernetes (CSI), tune performance (RDMA, NVMe-oF, GPU Direct Storage), lead reliability and operational practices, and provide staff-level architectural ownership and cross-team influence while executing hands-on storage engineering.

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.

Design, deploy, and operate production-scale AI/ML systems for personalization, semantic search, and RAG. Own vector database strategy, build embedding and retrieval pipelines, define BigQuery data models, architect AI services on GCP, and partner with product and marketing teams to deliver AI-driven customer and campaign capabilities.

Design, deploy, and maintain production-scale AI/ML systems for personalization, semantic search, and RAG. Own vector database strategy, data models in BigQuery, embedding and retrieval pipelines, and AI services on GCP. Partner with product and marketing teams, set engineering standards, and guide peers through design and reviews to integrate AI into CRM/CDP and marketing automation.