MLOps Engineering Manager

Purpose & Scope:

The ML Ops Engineering Manager is responsible for leading the delivery and operational excellence of ML Ops capability the infrastructure, pipelines, and practices that take machine learning and AI models from development into reliable, governed production use. This role manages a team of ML Ops engineers and partners closely with Data Science, Data Engineering, Platform, and Governance teams to deliver scalable, secure, and well-monitored model deployment and operations across growing AI portfolio. The ML Ops Engineering Manager focuses on execution, engineering rigor, team leadership, and cross-functional coordination, while model strategy and prioritization remain with Data Science and AI leadership.

Qualifications Required

• 8–12 years of experience in MLOps, ML engineering, DevOps, or platform engineering, including team or delivery leadership. • Strong hands-on background in CI/CD, containerization, and orchestration for ML workloads.

• Experience operating model registries, monitoring tooling, and ML pipelines in enterprise environments.

• Working knowledge of cloud platforms (Azure preferred) and Databricks-based ecosystems.

• Strong stakeholder management skills across data science, engineering, platform, and governance teams. Preferred

• Experience supporting AI/ML programs in retail, consumer goods, or other data-intensive industries.

• Familiarity with LLM/GenAI deployment patterns and evaluation practices. • Exposure to enterprise data governance and AI risk/compliance frameworks.

Success Measures

• Predictable, governed delivery of ML Ops capabilities with reduced rework. • Improved model deployment reliability, observability, and incident response.

• Increased reuse of standardized deployment patterns across model teams.

• High stakeholder confidence in the reliability and execution of the ML Ops function.