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

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