Staff Engineer- MLOps

Overview

Join Stryker's AI innovation unit as a Staff MLOps Engineer to design and run cloud-native ML systems on Azure Machine Learning that power computer vision and AI solutions for the operating room and other healthcare settings.

What you'll do

• Design, develop, and maintain high-performance, scalable MLOps systems and pipelines that interact with AI models and systems.
• Build and operate end-to-end ML workflows on Azure Machine Learning, including training pipelines, experiment tracking, model registry, and managed online/batch endpoints.
• Cooperate with cross-functional teams, including data scientists, AI researchers, and AI/ML engineers, to understand requirements, define project scope, and ensure alignment with business goals.
• Contribute technical expertise in evaluating, selecting, and implementing software technologies, tools, and frameworks in a cloud-native (Azure + Azure ML) environment.
• Troubleshoot and resolve intricate software problems, ensuring optimal performance and reliability when interfacing with AI/ML systems.
• Participate in software development project planning and estimation, ensuring efficient resource allocation and timely solution delivery.
• Contribute to the development of continuous integration and continuous deployment (CI/CD) pipelines.
• Contribute to the development of high-performance data pipelines, storage systems, and data processing solutions.
• Implement monitoring and observability for deployed models and platform services (performance, data/model drift, reliability), with dashboards and reporting in Power BI and Grafana.
• Contribute to the creation and maintenance of technical documentation, including design specifications, API documentation, data models, data flow diagrams, and user manuals.
• Mentor junior engineers and promote MLOps and software engineering best practices across the team.

What you'll need

• Bachelor's degree or equivalent in Computer Science, Engineering, or a related field.
• 7-10 years of relatable work experience, including a minimum of 4 years in machine learning operations or software/platform development.
• Strong knowledge of machine learning concepts, technologies, and integration techniques.
• Strong knowledge of programming languages commonly used in AI/ML, such as Python, R, or C++.
• Hands-on experience with Azure Machine Learning (workspaces, pipelines, compute, model registry, and endpoints) and the broader Azure cloud platform.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras, and model/experiment tracking tools such as MLflow.
• Experience with Agile methodologies (Scrum, Kanban) and project management tools such as Jira or Azure DevOps.
• Experience with version control systems, such as Git, and CI/CD tools, such as Jenkins, GitLab CI/CD, or Azure DevOps.
• Knowledge of containerization technologies like Docker and Kubernetes, and infrastructure-as-code tools such as Terraform or Azure Resource Manager (ARM) templates.
• Knowledge of data modeling techniques, and familiarity with data pipeline orchestration tools.
• Experience building monitoring and visualization dashboards with Power BI and Grafana.

Details

• Work flexibility: Hybrid.
• Travel: None.
• Location: Bengaluru, India.