DevOps Engineer

Candidate Portal | Machine Learning & AI Platforms We’re working with a venture-backed robotics and AI company developing remote-operated robotic systems that allow users to perform physical tasks from anywhere. Their platform combines real-time control, machine learning, and human-in-the-loop interfaces to improve how tasks are executed across distributed environments. The team is international and focused on building and deploying production-grade robotics systems used in real-world settings .

As an Machine Learning DevOps Engineer , you’ll sit at the intersection of machine learning, infrastructure, and product delivery. Working closely with R&D and Product teams, you’ll be instrumental in building scalable ML systems that power advanced robotic platforms. Responsibilities Build and maintain end-to-end CI/CD pipelines for ML model training, testing, and deployment Track model performance, accuracy, and latency; identify data and concept drift Design and optimise scalable cloud and on-prem environments (AWS, GCP, Azure), using Docker and Kubernetes Develop and maintain dashboards for live system configuration, monitoring, and fleet management Support data ingestion, preprocessing, and feature store development Implement robust versioning across models, data, and code, ensuring compliance and reproducibility Work closely with data scientists to productionise ML models into API-driven services Requirements 4–7 years’ experience in MLOps, ML Engineering, or DevOps Strong Python skills, with Bash/shell scripting experience Full-stack experience (React, TypeScript, Express.js, PostgreSQL) Familiar with ML frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with cloud platforms (AWS SageMaker, Azure ML, or Vertex AI) Solid knowledge of Docker and Kubernetes Exposure to MLOps tools (MLflow, Kubeflow, Airflow, DVC) Degree in Computer Science, Data Science, or similar Strong problem-solving and communication skills #J-18808-Ljbffr