Designation: MLOps Lead

Educational Qualification

- B.Tech / M.Tech / M.S. in Computer Science, Data Engineering, AI, or a related discipline.
- Certification in cloud DevOps or MLOps platforms (AWS DevOps Engineer, Azure DevOps Expert, GCP Skilled ML Engineer) is highly desirable.
- Contributions to MLOps or DevOps open-source projects are preferred.

Experience

- 7–10 years of experience in Machine Learning Operations (MLOps) or DevOps engineering.
- Minimum 4 years of experience building CI/CD pipelines for AI/ML model deployment in enterprise or government ecosystems.
- Proven experience with containerized applications and microservices architectures.

Key Responsibilities

- Design and manage Continuous Integration and Continuous Delivery (CI/CD) pipelines for AI/ML models across multiple environments.
- Establish model versioning, deployment, monitoring, and rollback mechanisms to ensure system stability and traceability.
- Automate model training, testing, and serving workflows using containerized solutions.
- Define Infrastructure-as-Code (IaC) templates for scalable AI/ML deployment across on-premises and cloud environments.
- Collaborate with Data Science and Engineering teams to standardize model input/output formats and performance metrics.
- Implement logging, monitoring, and alerting for deployed models to ensure high availability and accuracy over time.




- Ensure compliance with Responsible AI guidelines for deployment, including bias auditing and explainability tracking.

Technical Competencies

- MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP Vertex AI Pipelines for end-to-end ML workflow orchestration.
- Containerization: Docker, Kubernetes, Helm charts, container registries, and microservices architecture for ML workloads.
- CI/CD: Jenkins, GitLab CI, GitHub Actions, Azure DevOps, with specialized ML pipeline integration and automated testing.
- Infrastructure-as-Code: Terraform, CloudFormation, Ansible for reproducible ML infrastructure provisioning and management.
- Cloud Platforms: AWS (EKS, Lambda, ECR, S3), Azure (AKS, Container Registry, Blob Storage), GCP (GKE, Cloud Build, Cloud Storage).
- Model Serving: TorchServe, TensorFlow Serving, Seldon, KServe, REST APIs, and real-time inference infrastructure.
- Programming Languages: Python for automation, Bash scripting, YAML for configuration management, and a basic understanding of Go/Java.
- Database & Storage: Feature stores (Feast, Tecton), model registries, data versioning (DVC), and distributed storage systems.
- Workflow Orchestration: Apache Airflow, Prefect, Argo Workflows for complex ML pipeline scheduling and dependency management.

Pay: ₹1,500,000.00 - ₹1,800,000.00 per year

Work Location: In person

📌 MLOps Lead (Delhi)
🏢 RamAI
📍 Delhi