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