ML Ops Lead

Corover; Delhi; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Corover in Delhi seeks an experienced MLOps Engineer to design and manage CVCD pipelines for AI/ML models across environments, ensuring scalable, reliable deployment.

You will implement model versioning, monitoring, and rollback, automate training and serving, and define IaC templates for on-prem and cloud deployments, collaborating with data science teams.

Join to uphold Responsible AI practices with bias auditing and explainability tracking, contributing to robust, compliant ML operations.

Qualifications

• 4+ years building CVCD pipelines for AI/ML deployments.
• Experience with containerized microservice architectures.
• Knowledge of on-prem and cloud deployment.
• Certification in cloud DevOps or MLOps desirable.
• Contributions to MI-Ops or DevOps open source projects preferred.

Responsibilities

• Design and manage continuous integration and delivery (CVCD) pipelines for AI/ML models across multiple environments.
• Establish model versioning, deployment, monitoring, and rollback mechanisms to ensure stability and traceability.
• Automate training, testing, and serving workflows using containerized solutions.
• Define infrastructure-as-code templates for scalable AI deployment on on-prem or 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.

Skills

MLOps

DevOps

CI/CD

Containerization

Kubernetes

Python

Automation

Model monitoring

On-prem/cloud deployment

Education

Bachelor/Master in CS/EE/AI

Cloud DevOps or MLOps certification

Tools

MLflow

Kubeflow

Azure ML

AWS SageMaker Pipelines

GCP Vertex AI Pipelines

Docker

Kubernetes

Terraform

Jenkins

GitHub Actions

Airflow

Seldon/KServe

Job description

Educational Qualification:

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

Experience:

• 710 years in machine learning operations or DevOps engineering.
• Minimum 4 years building CVCD pipelines for AI/ML model deployment in enterprise or government ecosystems.
• Proven experience with containerized and microservice architectures.

Key Responsibilities:

1. Design and manage continuous integration and delivery (CVCD) pipelines for AIIML models across multiple environments.

2. Establish model versioning, deployment, monitoring, and rollback mechanisms to ensure stability and traceability.

3. Automate training, testing, and serving workflows using containerized solutions.

4. Define infrastructure-as-code templates for scalable AI deployment on on-prem or cloud environments.

5. Collaborate with Data Science and Engineering teams to standardize model input/output formats and performance metrics.

6. Implement logging, monitoring, and alerting for deployed models to ensure high availability and accuracy over time.

7. Ensure compliance with Responsible Al guidelines for deployment, including bias auditing and explainability tracking.

Technical Competencies:

• MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP Vertex Al Pipelines for end-toend 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, 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