Top Artificial Intelligence Jobs in Czechia

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

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 Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

Lead technical direction and architecture for global web platforms, driving standards, secure scalable architectures, CMS integrations, AI/GenAI adoption, CI/CD/DevOps practices, automated testing, and agile delivery across cross-functional teams.

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.

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.

Manage day-to-day procurement payments operations ensuring SLA, tax, legal and policy compliance. Collaborate with procurement teams and business partners, coach junior colleagues, drive process improvements, support automation/AI initiatives, and maintain strong stakeholder relationships to enhance user experience and operational efficiency.

Lead strategy and delivery for QAD ERP and related enterprise applications. Drive roadmap, integrations, data governance, operational excellence, security compliance, change management, and global team development.

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.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

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 development and deployment of AI solutions, translating business needs into product requirements, managing backlogs, and coordinating cross-functional teams.

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.

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

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.

Develop, integrate, and support AI/LLM-powered applications and agent workflows. Assist with prompt engineering, context management (RAG/vector search), Python-based APIs and data pipelines, testing, monitoring, and collaboration with product and data teams under senior supervision.

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.

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.