Medtronic is looking to build and scale image analytics MLOps capabilities to enable visual inspection, automation, and insights from visual data across its diverse product portfolio in manufacturing.
Requirements
- Machine Learning / MLOps / Software Engineering with a focus on computer vision applications
- AWS cloud services, especially SageMaker, S3, EKS, Lambda, and CloudFormation/Terraform
- Kubernetes and containerization (Docker, Helm, GPU orchestration) for AI/ML model deployment
- Developing end-to-end ML pipelines, including CI/CD, experiment tracking, and model/data versioning (e.g., MLflow , Sagemaker )
- Python and sufficient understanding of AI /DL frameworks ( PyTorch , TensorFlow)
- Deploy computer vision models in production, including optimization for GPU inference ( TensorRT , ONNX Runtime, Triton)
- Model monitoring and observability for drift, performance, and compliance in regulated environments
Responsibilities
- Design, implement, and maintain model training, evaluation, and deployment pipelines for computer vision applications, leveraging AWS SageMaker, Triton Inference Server, and Kubernetes-based orchestration.
- Partner with Vision , Manufacturing, and Automation teams to translate feasible models into production-ready inspection systems.
- Build scalable image data pipelines for ingestion, annotation, and curation, ensuring datasets are reliable, versioned, and reproducible.
- Integrate ML services into manufacturing workflows, bridging OT (Operational Technology) with IT/Cloud platforms.
- Implement model observability and monitoring solutions, tracking performance, drift, latency, and GPU utilization for continuous improvement.
- Collaborate on continuous improvement initiatives, including CI/CD for ML models, reproducibility, infrastructure-as-code, and workflow automation.
- Provide technical leadership and mentorship in MLOps best practices, supporting both centralized and site-level adoption across Medtronic’s global footprint.
Other
- Minimum of 4 days a week onsite
- Strong collaboration skills across Data Science, Manufacturing Engineering, and IT functions
- The ability to problem solve and troubleshoot machine learning systems
- Experience with edge deployment (e.g., NVIDIA Jetson/Orin, DeepStream , or hybrid cloud/on-prem architectures)
- Familiarity with manufacturing automation systems (PLC, SCADA, OPC-UA, MQTT) and integrating ML services into production lines
