Senior Machine Learning Ops Engineer (AI / ML Data Scientist) (Remote)

A Bachelor’s or Higher Degree is the minimum entry required for the position.

Overview

Job Title : Senior Machine Learning Ops Engineer

Job Description : We are seeking a highly skilled MLOps Engineer to build, scale, and support our Google Vertex machine learning platform to enable a multi-model serving environment. This role is central to building reusable infrastructure components (i.e., infra as code), model deployment pipelines, and providing a collection of templates that accelerate model deployment, monitoring, and support for domain teams. You will collaborate closely with data scientists, other MLOps engineers, and product teams to ensure that models are deployed efficiently, observable, maintainable, and aligned with business goals. Your work will empower domain teams to independently run, support, and monitor their models using platform-provided tools and best practices. This role is part of a larger ML platform team that supports product recommendation capabilities and will work alongside Data Scientists, Data Engineers, Machine Learning Engineers, and software engineers to build, test, maintain, and support data pipelines, ML models, and back-end services that make up our product recommender platform.

Responsibilities

• Design and implement reusable modules and templates for model training, deployment, and monitoring across Vertex AI and other cloud platforms.
• Build and maintain scalable CI / CD pipelines for ML workflows, enabling rapid iteration and safe promotion across environments.
• Develop tooling and automation to support overall platform observability, including drift detection, performance tracking, request latency, and alerting.
• Partner with domain teams to onboard models into the platform, ensuring alignment with operational standards and SLAs.
• Maintain and evolve the feature store, model registry, and endpoint management systems to support high-throughput, low-latency inference.
• Collaborate with leadership to define and enforce governance policies, including versioning, rollback strategies, and access controls.
• Provide technical guidance and support to domain teams, enabling self-service capabilities and reducing operational bottlenecks.
• Work closely with data scientists to understand their needs and efficiently integrate their models into production systems.
• Act as a liaison between the data engineering, data science, MLEs, MLOps, and leadership teams, facilitating seamless communication and goal alignment.

Qualifications

Preferred Skills

#J-18808-Ljbffr