Top Artificial Intelligence Jobs in Czechia

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

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.

Develop and evaluate imitation- and reinforcement-learning models from human-demonstration data, build scalable reproducible training and evaluation pipelines, collaborate with RAI, hardware, and data teams, and move research results into production on humanoid robots.

Train and evaluate deep learning models on human-demonstration data, build scalable and reproducible training/evaluation pipelines, collaborate with RAI, hardware and data teams, and move research results into production on physical robots.

Design and improve reinforcement-learning and post-training methods for robust robot policies. Train and evaluate models on human-demonstration data, build scalable training and evaluation pipelines, collaborate with RAI, hardware, and data teams, and deploy research results to production robots.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

Build and ship agent orchestration workflows, integrations, and shared infrastructure for AI-driven growth across paid media, lifecycle, organic content, CRO, incentives, and referrals. Implement monitoring, audit logging, creative generation pipelines, and reliable integrations so domain experts can tune and operate workflows independently.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

As a Senior AI Engineer, you will develop AI-powered features, integrating LLM APIs to enhance user workflows while collaborating with teams to ensure reliability and scalability.

Own and build the Apify MCP Server to expose Actors as reliable agent-callable tools, maintain evolving protocols and authorization, shape the Apify agent runtime, enable agent discovery and payments, and create evals from production traces to catch regressions and measure product changes.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

The role involves leading design for AI-native products at Synthesia, focusing on improving interaction patterns and enhancing user experience in AI-driven workflows.

Designs and deploys production-grade generative AI, RAG, and agentic systems for enterprise clients. The role combines client workflow discovery, architecture, production coding, cloud-native AWS delivery, evaluation and monitoring, cost optimization, technical scoping, stakeholder communication, and knowledge transfer. Candidates must independently solve ambiguous problems, engage executives and engineers, and own technical outcomes across industries including financial services, insurance, healthcare, and life sciences.

Build and deploy production-grade generative AI and agentic systems for enterprise clients. Responsibilities include developing RAG applications, evaluation harnesses, backend services, data pipelines, and cloud-native AWS deployments; implementing LLMOps and AgentOps practices; monitoring quality, drift, cost, and latency; integrating APIs; improving reliability; contributing to architecture and reusable blueprints; and mentoring AI engineers in a forward-deployed consulting environment.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

Leads customer engagements involving Google Cloud data platforms, machine learning, and Generative AI. Designs architectures, oversees delivery teams, develops production-ready AI pipelines, and establishes MLOps and LLMOps practices. Advises on LLM integration, RAG, prompt engineering, and foundation models while supporting presales, internal offerings, mentoring, and knowledge sharing.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Design, build, and deploy AI and GenAI/LLM solutions (including RAG and agentic systems) into production. Own full ML lifecycle: data preparation, model development, deployment, monitoring, and optimization for scalability, performance, cost, and user experience. Work in agile teams to scale solutions across countries and integrate with cloud and API services.

The AI Legal Specialist will manage compliance with global laws related to AI and e-commerce, focusing on contracts and data protection. This involves preparing compliance documentation, conducting risk assessments, and maintaining day-to-day compliance tasks while collaborating closely with the Ethics and Compliance Officer.

Join the Compliance team to own AI and e-commerce legal workflows: GDPR and data protection documentation, AI systems inventory, risk assessments, product/feature reviews, contract and IP issue handling, TOS/privacy updates, and monitoring legal developments. Work with the Ethics & Compliance Officer and review uncensored AI content as needed.