Top Machine Learning Jobs in Czechia

Lead a multi-agent GenAI PoC for an FMCG client: own client relationship, design solution architecture (Discovery/Ideation/Validation/Orchestration), define retrieval and evaluation strategies, run benchmarking with SMEs, manage PoC/pilot delivery, and shape follow-on phases including conversational layers and scaled evaluation.

The role involves shaping the technical direction of a core domain, leading architectural design, ensuring system security and scalability, and embedding AI in developer workflows while supporting various engineering teams.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build, deploy, and maintain production-scale ML and real-time AI services. Collaborate with cross-functional teams to design MLOps pipelines, automate ETL and retraining, benchmark models, document methodologies, and drive ML/LLM Ops best practices to deliver business value.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Design, build, and maintain MLOps tools, automation, and end-to-end ML pipelines. Support large-scale GPU clusters, implement monitoring/observability, streamline reproducible training and deployment, and collaborate with product and engineering teams to optimize ML workflows for scalability and cost-efficiency.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Join a cross-functional team to design and implement AI agents and LLM-powered applications. Use GitHub Copilot, Python, and prompt engineering to build agent workflows, integrate tools, and leverage technical documentation as knowledge sources.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Design, build, and maintain production ML pipelines and data pipelines, train and optimize large-scale personalization and recommendation models, deploy and operate low-latency model serving, monitor production model performance, run A/B experiments, and collaborate across data science, engineering, and product teams to continuously improve personalization systems.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Provide EU-focused data privacy and digital regulatory legal counsel across business segments. Lead privacy program development, support HR, Marketing, M&A, and transactional work, manage incident response, regulatory engagement, DPIAs, cross-border data transfers, and advise on EU Data Act, GDPR, data governance, and product compliance.

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.

Train and evaluate models on human-demonstration data, build training and evaluation pipelines, and deploy learned policies to physical humanoid robots.

Lead a small senior ML team to convert human-demonstration data into robot behavior. Own force-expert and VLA training direction, drive models from research to production on physical robots, and collaborate with engineering to deploy learned policies on hardware.

Develop and validate decoders that map high-density surface EMG into continuous force and joint angles, exploit a 30–100 ms intent window, design data capture and labeling protocols, run experiments on signal quality and generalization, and collaborate with robotics ML team for real-time integration.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

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.

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.