Top Artificial Intelligence Jobs in Czechia

Build and scale low-latency AI serving infrastructure for SLMs, prompt-injection engines, and classification services. Architect GPU-accelerated inference pipelines, backend microservices, Kubernetes clusters, rate limiting, tenant guardrails, monitoring, and autoscaling systems. Develop continuous evaluation, retraining, model CI/CD, regression gates, and MLOps/LLMOps pipelines while improving service reliability, latency, cost efficiency, and detection quality.

Designs and delivers production-grade generative AI, RAG, and agentic systems for enterprise clients. The role combines hands-on software development, cloud-native AWS deployment, evaluation and monitoring, architecture leadership, technical scoping, client engagement, and change management. Engineers work directly with executives and technical teams, own technical outcomes, mentor others, and transfer maintainable systems to clients.

Build and ship production generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, evaluation harnesses, backend services, data pipelines, and cloud-native deployments; implementing LLMOps and AgentOps monitoring, tracing, guardrails, drift detection, and cost controls; making architecture decisions; documenting and handing over solutions; and mentoring AI engineers.

Six-month internship focused on applying agentic AI to web GUI and API test generation. Responsibilities include building AI agents, evaluating generated test cases for accuracy and maintainability, integrating MCP servers, shadowing development and QA teams, documenting results, and proposing AI-driven workflow improvements.

Own the production architecture of an AI-assisted KYC and customer due diligence platform across Azure and Databricks. Responsibilities include designing networking, identity, environments, integrations, resilience, monitoring, backup and rollback; defining operating models and support processes; planning service transition; and representing the solution to architecture boards and senior stakeholders.

Build and operate AI-powered tools and workflow automations for business teams across Support, Finance, Sales Ops, HR, IT, and Legal. Analyze workflows, prioritize high-impact opportunities, develop reliable integrations, measure outcomes, protect sensitive data, evaluate vendors, and establish AI governance, documentation, training, and deployment processes.

Build and maintain scalable cloud AI platforms for sourcing applications. Partner with research and product teams to develop data pipelines, feature storage, experiment tracking, model lifecycle workflows, monitoring, and reliable ML services. Design infrastructure for model training, evaluation, and production inference while improving experimentation, benchmarking, and reproducibility. Contribute to platform architecture, documentation, and long-term strategy.

Lead end-to-end applied machine learning research for sourcing, from problem definition and dataset creation through experimentation, evaluation, and production deployment. Develop recommendation, forecasting, optimization, market design, and multimodal document-understanding systems using large-scale spend data. Set research direction, establish trustworthy benchmarks, mentor researchers and engineers, collaborate with Product and AI Platform teams, and ensure models meet production requirements for cost, latency, robustness, and business impact.

Develop Responsible AI governance tooling, technical guardrails, risk assessments, MCP scanners, and compliance automation for AI systems and agents. Build production-grade software integrated with CI/CD pipelines, support OSS compliance workflows, improve security and reliability, and translate governance requirements into engineering solutions. The role also involves technical documentation, training, stakeholder communication, and establishing organization-wide Responsible AI practices.

Build and deploy production generative AI systems, including RAG pipelines, agentic workflows, document understanding, document generation, and conversational interfaces. Own AI architecture, implementation, evaluation, testing, observability, and deployment while collaborating with product, design, and engineering teams to deliver reliable user-facing functionality for legal technology products.

Design and build scalable developer tools and services, integrate AI-assisted features (e.g., Copilot, LLM tools), dogfood internal AI platforms, collaborate across teams and open-source communities, participate in reviews and architecture, and research emerging AI techniques to improve developer productivity.

Develop and deploy AI services across finance, HR, sales, service, legal, and quality assurance. Identify and prioritize use cases, build LLM and RAG solutions, integrate them into business processes, guide external partners, communicate technical decisions, and support organization-wide AI adoption.

Build and ship production AI features for network assurance use cases including change assurance, troubleshooting, compliance, and vulnerability detection. Own implementation, testing, deployment, and delivery of assigned features while contributing to shared AI infrastructure. Work with MCP and agentic frameworks, evaluate trade-offs, participate in technical reviews, and share AI engineering practices across the team.

Own and improve the company’s AI Gateway, including deploying models, integrating third-party providers, monitoring usage and costs, and optimizing routing and context compression. Make repositories agent-ready through skills, instructions, linters, CI guardrails, and software-factory capabilities. Build dashboards and internal tools, support AI adoption, contribute to product AI features, and partner with IT, Finance, and Legal on security and data governance.

Designs and implements AI-powered legal workflows and Libra Assistants for law firms and corporate legal departments. Partners with clients and cross-functional Product, Engineering, Sales, and Customer Success teams to configure solutions, support pilots, translate legal reasoning into prompts and structured workflows, create enablement content, and provide feedback to improve AI quality, accuracy, and product capabilities.

Develop and optimize prompts, guardrails, and agentic AI workflows for high-voltage product use cases. Translate GIS and AIS expertise into reliable AI applications, evaluate prompt performance, collaborate with engineering and commercial teams, align solutions with RAG pipelines and enterprise data, and support user adoption, training, documentation, and regulatory compliance.

Build and operate production AI products, including sales acceleration systems, recommendation engines, and agentic operating systems. Own solutions end to end by gathering user needs, designing, coding, deploying, monitoring, and maintaining them. Evaluate agent decisions, detect performance drift, enable non-engineers to create automations, and contribute to shared AI infrastructure. Work across TypeScript, Rust, Ruby, Google Cloud, Kubernetes, and infrastructure as code while collaborating across advisory, sales, operations, and engineering.

Lead the strategy and roadmap for Wrike’s AI Platform and Ecosystem, including MCP, Skills, and GraphQL. Guide engineering teams, establish standards for secure and reliable agent interactions, enable internal teams to create agent-ready tools, and manage MCP plugin distribution across AI marketplaces such as OpenAI, Anthropic, and Microsoft. Use customer research, adoption data, and ecosystem feedback to prioritize improvements while translating complex technical trade-offs into product decisions.

Designs and delivers AI-native solutions across business functions including People, Legal, Finance, and Operations. Responsibilities include identifying transformation opportunities, building and deploying AI agents and automated workflows, integrating enterprise systems, driving adoption, measuring business impact, applying governance and human oversight, and mentoring colleagues. The role partners with functional leaders to redesign operating models and continuously improve production AI solutions.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.

Architects and delivers enterprise AI, machine learning, generative AI, RAG, and agentic systems. Designs secure end-to-end architectures, integrates Claude and other models with ServiceNow, APIs, databases, cloud platforms, and workflows, and leads discovery, proofs of concept, and client delivery. Develops ML and NLP solutions, data pipelines, dashboards, evaluation frameworks, observability, governance, and responsible AI controls. Collaborates with stakeholders, engineers, consultants, and clients to operationalize reliable AI applications.

Build and productionize agentic AI systems, RAG pipelines, LLM evaluation frameworks, safety guardrails, and classical ML models. Own LLMOps/MLOps, observability, monitoring, and Azure-based deployment. Integrate agents with enterprise systems, translate business needs into AI solutions, present technical decisions, mentor junior data scientists, and lead engineering best practices.

Build and productionize agentic AI and NLP systems that process millions of documents and support data operations. Develop tool-calling agent loops, orchestration, memory management, RAG pipelines, metadata and entity extraction, and evaluation frameworks measuring accuracy, latency, and cost. Apply Python, SQL, and Snowflake to assess solutions, implement guardrails, manage AI risks, collaborate with technical stakeholders, and communicate delivery trade-offs.

Lead Nuitee’s agentic AI strategy and capabilities for searching, booking, paying for, modifying, and canceling hotel stays. The role combines hands-on software engineering, product development, technical leadership, commercial opportunity identification, and team building. Reporting to the CEO, this leader will design secure agent-native APIs and MCP tools, improve transaction reliability, and take new products from concept to production across Nuitee’s travel infrastructure.