Capgemini Looking For Mlops Lead For Bangalore/Hyderabad/Pune

MLOps Engineer to lead the design and implementation of scalable, secure, and production-grade ML platforms across multi-cloud environments (Azure, AWS, GCP).

The role involves architecting end-to-end ML lifecycle solutions, enabling reproducibility, governance, and operational excellence while working closely with data science, platform engineering, and cloud teams.

1. overall experience and dedicated MLOps / AI Platform Engineering experience
2. Kubernetes hands-on expertise
3. Python development experience
4. CI/CD implementation experience
5. Monitoring and observability experienceRole & responsibilities

We are looking for a lead MLOps Engineer lead the design and implementation of scalable, secure, and production-grade ML platforms across Azure platform.

The role involves architecting end-to-end ML lifecycle solutions, enabling reproducibility, governance, and operational excellence while working closely with data science, platform engineering, and cloud teams.

Must-Have Skills

1. Cloud Expertise

• Experience in
• Azure: Azure ML, Azure DevOps, AKS, Functions, Logic Apps, Key Vault
• Databricks: Databricks asset bundles, Unity Catalog, Lakehouse Monitoring, Databricks clusters

2. CI/CD for ML and LLMs

• Build and manage ML pipelines using:
• Azure DevOps / Databricks Asset Bundles
• Implement:
• Model build, test, validation, and deployment workflows
• Integration with container registries and artifact stores

3. Model Lifecycle Management

• End-to-end lifecycle:
• Experiment tracking (MLflow)
• Model versioning, registry, packaging, deployment, rollback
• Exposure to model governance and lineage tracking Databricks Unity Catalog and Lakehouse monitoring

4. Infrastructure as Code (IaC)

• Hands-on experience with:
• Terraform (preferred cross-cloud standard)
• Automating infrastructure provisioning for ML workloads

5. Containerization & Orchestration

• Strong experience in:
• Docker-based packaging
• Kubernetes platforms: AKS
• Deploying scalable ML inference services

6. Automation & Orchestration

• Build automated workflows for:
• Training, validation, deployment, retraining
• Experience with orchestration tools:
• Airflow / Azure Container Apps

7. ML Observability & Monitoring

• Monitoring tools across cloud ecosystems:
• Azure Monitor, Application Insights
• Open-source: Prometheus, Grafana
• Implement:
• Model performance, drift detection, alerting

8. Feature Store & Data Integration

• Experience with feature stores:
• Azure / Databricks Feature Store
• Designing reusable, governed feature pipelines

9. Model Deployment Platforms

• Experience deploying models using: Azure ML
• REST API-based inference endpoints and microservices

10. Programming & APIs

• Strong Python skills (automation, pipelines, ML integration)
• Experience building and consuming REST APIs / microservices

11. Scrum + Agile Delivery

• Certified Scrum Master with 2+ years experience facilitating agile ceremonies and managing structured sprint execution,
• Experience in removing blockers and enabling cross-functional team collaboration
• Experience in aligning sprint outcomes with product roadmap and driving continuous improvement

Good-to-Have Skills

• Multi-cloud MLOps framework/tooling contributions
• Experience with:
• Kubeflow, ZenML, KServe, ONNX, Triton Inference Server
• Security and vulnerabilities
• Exposure to:
• Real-time/streaming ML (Kafka, Kinesis, Pub/Sub)
• Responsible AI / governance frameworks
• Cost optimization (FinOps practices across clouds)

Preferred candidate profile