MLOps/LLMOps Lead Engineer with Surgical Video ,DICOM and media

Job Title: MLOps/LLMOps Lead Engineer

Location: Remote with occasional travel to Santa Clara, CA

We're looking for a MLOps Engineer to join a high-impact healthcare initiative with our client, a world-renowned leader in medical technology. You'll be part of a team driving the cloud migration of a secure digital surgical ecosystem from Azure to AWS.

This platform is at the heart of a next-generation surgical experience, connecting data, devices, and teams to power advanced analytics and machine learning models. You will be responsible for building, automating, and maintaining the infrastructure that supports the entire machine learning lifecycle, from data ingestion to model deployment and monitoring. In this role, you'll help build a secure, resilient, and compliant environment for critical healthcare applications that leverage AI and machine learning.

Required Skills:

• MLOps Expertise: Proven experience in building and managing machine learning pipelines in a production environment.
• AWS Expertise: Extensive hands-on experience with core AWS services (S3, SageMaker, EKS, Lambda, CloudFormation, etc.).
• DevOps Practices: Deep understanding of CI/CD, automation, and infrastructure as code (IaC) using Terraform or CloudFormation.
• Containerization: Solid experience with Docker and Kubernetes for orchestrating ML workloads.
• Scripting: Proficiency in Python and other scripting languages for automation.
• Data Engineering: Familiarity with data processing workflows and tools for handling large datasets.

Nice-to-Have Skills:

• Media Experience: Experience with AWS Media Services (e.g., AWS Elemental, Kinesis Video Streams) or a background in managing video data.
• Surgical/Medical Context: Prior experience with medical devices, DICOM standards, or healthcare data is a plus.
• Machine Learning Frameworks: Familiarity with frameworks like TensorFlow, PyTorch, or scikit-learn.

This is your chance to work on a product that impacts lives, leverages modern cloud technology, and prioritizes security, performance, and reliability.

Key Responsibilities:

• Design and implement MLOps pipelines for the training, testing, and deployment of machine learning models on AWS.
• Automate the end-to-end machine learning lifecycle, including data versioning, model training, and continuous integration/continuous delivery (CI/CD) for model updates.
• Manage and optimize the cloud infrastructure on AWS to ensure scalable and cost-effective model serving and inference.
• Collaborate with data scientists and machine learning engineers to streamline the transition of models from development to production.
• Establish robust monitoring and logging for model performance, data drift, and system health in a production environment.
• Ensure the security and compliance of the ML platform, adhering to healthcare industry standards.
• Provide technical leadership and guidance on MLOps best practices, tools, and workflows.