MLOps Lead

ROLE OVERVIEW
The MLOps Lead will define and govern the engineering platform, deployment standards, automation, monitoring, and lifecycle controls required to run AI/ML services reliably across enterprise and government settings.

Educational Qualifications
B.Tech., M.Tech., or M.S. in Computer Science, Data Engineering, AI, or a related discipline.
Cloud DevOps or MLOps certifications such as AWS DevOps Engineer, Azure DevOps Expert, or GCP Skilled ML Engineer are highly desirable.
Contributions to MLOps or DevOps open-source projects are preferred.

Experience
710 years in MLOps, DevOps, platform engineering, or related roles.
At least 4 years building CI/CD pipelines for AI/ML deployment in enterprise or government ecosystems.
Proven experience with containerised, microservice-based, cloud, hybrid, or on-premise architectures.

Key Responsibilities
Design and govern CI/CD and continuous-training pipelines for AI/ML models across multiple environments.
Establish standards for model versioning, packaging, deployment, monitoring, rollback,



approvals, and traceability.
Automate training, validation, testing, security checks, serving, and release workflows using containerised solutions.
Define infrastructure-as-code templates and reusable platform components for scalable on-premise and cloud deployment.
Collaborate with data science, engineering, architecture, security, and product teams to standardise model contracts, input/output formats, metrics, and release criteria.
Implement logging, monitoring, alerting, SLOs, incident handling, capacity management, and reliability controls for deployed models.
Lead model-serving architecture and select fit-for-purpose orchestration, feature-store, registry, workflow, and observability technologies.
Ensure Responsible AI deployment controls, including bias-evaluation evidence, explainability tracking, lineage, and approval records.
Mentor MLOps engineers, review platform design

📌 Mlops Lead Delhi
🏢 Expertshub.ai
📍 Delhi