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

Positive-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

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