Engineering Manager - MLOps

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.

What you will be doing (responsibilities) :

ML Ops Delivery Leadership :

• Lead end-to-end delivery of CI/CD pipelines, model registry practices, and deployment infrastructure for ML and AI use cases.
• Drive predictable execution of the ML Ops roadmap in partnership with Data Science and Platform leadership.
• Establish and enforce engineering standards for model packaging, testing, deployment, and rollback.
• Proactively manage delivery risks, technical dependencies, and production incidents across deployed models.

Platform &

• Architecture Alignment :
• Ensure ML Ops solutions align with enterprise data and AI platform standards and architecture patterns.
• Partner with platform and architecture teams to design scalable, cost-effective serving and training infrastructure.
• Guide teams on appropriate use of shared compute, environments, and model infrastructure.

Monitoring, Governance &

• Trust :
• Embed model monitoring, drift detection, and performance alerting into pipelines as standard practice.
• Ensure model versioning, lineage, and documentation requirements are met to support auditability.
• Partner with data governance, security, and compliance teams to ensure responsible and compliant AI deployment.

Engineering Excellence &

• Operational Readiness :
• Drive CI/CD maturity for ML pipelines, including automated testing, staged rollouts, and controlled promotions.
• Ensure deployed models are operationally ready with monitoring, alerting, and clear incident ownership.
• Continuously improve reliability, latency, and cost-efficiency of training and inference workloads.

Stakeholder &

• Cross-Functional Collaboration :
• Partner with Data Science and AI leadership to translate model roadmaps into executable engineering deliverables.
• Collaborate with Data Engineering to ensure consistent, high-quality data feeds into ML pipelines.
• Communicate delivery status, risks, and trade-offs clearly to stakeholders and leadership.

People Leadership &

• Team Development :
• Manage, mentor, and develop a team of ML Ops engineers across experience levels.
• Set clear expectations around quality, delivery discipline, and operational ownership.
• Foster a culture of automation, documentation, and continuous improvement.

What you bring (Qualifications) :

Required :

• 8 - 10 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.