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 environments.

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 designs, and manage technical risks and production readiness.

Technical Competencies

- MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, and GCP Vertex AI Pipelines.
- Containerisation and Serving: Docker, Kubernetes, Helm, container registries, KServe, Seldon, TorchServe, TensorFlow Serving, and REST APIs.
- CI/CD: Jenkins, GitLab CI, GitHub Actions, and Azure DevOps with ML-specific testing and approval gates.
- Infrastructure-as-Code: Terraform, CloudFormation, and Ansible.
- Cloud: AWS EKS/Lambda/ECR/S3, Azure AKS/ACR/Blob Storage, and GCP GKE/Cloud Build/Cloud Storage.
- Programming: Python, Bash, YAML, and working familiarity with Go or Java.
- Data and Storage: feature stores, model registries, DVC, distributed storage, and lineage management.
- Workflow Orchestration: Apache Airflow, Prefect, and Argo Workflows.
- Leadership: platform governance, architecture review, technical mentoring, incident management, and stakeholder communication.

📌 MLOps Lead (Delhi)
🏢 Expertshub.ai
📍 Delhi