Engineering Manager - MLOps in India, भारत

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 https://jobeax.com/link/7oo1eEybyLdI9X5i 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 https://jobeax.com/link/3z5VAy9Q5Vti7gkf 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 https://jobeax.com/link/ZCp06r3j1eabf1Iy 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 https://jobeax.com/link/QtHugtBOItQaFRpT & 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 https://jobeax.com/link/650vgIlKrG3vfw81, 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 https://jobeax.com/link/ANvCNVIkd7A8bFUB 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 https://jobeax.com/link/9ERSu6ibGcN2j0tE & 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 https://jobeax.com/link/V0b46LH6MWgMySan 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 https://jobeax.com/link/CmImXHtuf7P9F8ph 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 https://jobeax.com/link/TKg5uFi8EXixZol0 :- 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 https://jobeax.com/link/jftslDiRZDNtHe3S 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. (ref:hirist.tech)