As a valued member of Stryker’s AI innovation unit, you will work alongside trailblazers, industry visionaries, innovators, and inventors who are committed to bringing computer vision, AI/ML and digital innovation to the operating room and other healthcare settings. You’ll contribute to fast-paced cycles of innovation and develop core technologies that power a wide array of Stryker’s solutions, including: surgical robotics and navigation, image-guided surgery, treatment selection, outcome assessment, and clinical decision intelligence. You will apply your core skills across a range of deployment platforms spanning from mobile applications, cloud services, and SDKs to embedded systems, edge devices, and mixed reality (XR) platforms. You will have an opportunity to work across a wide variety of therapeutic areas ranging from orthopedics and neurosurgery to emergency care and operating room safety and efficiency – plus many more.

We are in search of a skilled and experienced Staff MLOps Engineer to join our team, with a focus on building systems and applications that interact with AI models in a cloud-native Azure environment, with Azure Machine Learning (Azure ML) at the core of our ML platform. In this position, you'll be responsible for designing, developing, and maintaining software applications that flawlessly integrate with state-of-the-art AI and machine learning technologies.

What You Will 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 Will Need:

Minimum Qualifications

·       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.