Senior Machine Learning Engineer

Job Title:

Senior MLOps Engineer (Databricks | MLflow | Cloud | PySpark)

Job Function:

MLOps Machine Learning Engineering Databricks Engineering Cloud & DevOps CI/CD Automation

Work Mode & Location:

Location: Ghansoli, Mumbai (Preferred)

Remote flexibility available

Experience:

46 years

Budget:

Up to 1 Lakh Per Month (1 LPM)

Job Summary:

We are seeking an experienced Databricks MLOps Engineer to join our Data and AI Engineering team. The ideal candidate should have strong expertise in Databricks, MLflow, cloud platforms, and end-to-end MLOps automation. You will collaborate with data scientists, ML engineers, and business stakeholders to build scalable and reliable production ML pipelines.

Key Responsibilities

1. Databricks Platform Management

• Work with Databricks Workspaces, Jobs, Workflows, Unity Catalog, Delta Lake, and MLflow.
• Optimize Databricks clusters, compute usage, permissions, and workspace configuration.

2. End-to-End MLOps Lifecycle

• Manage model training, versioning, deployment, monitoring, and retraining processes.
• Implement deployment strategies including A/B testing, blue-green, and canary releases.

3. Programming & ML Development

• Develop ML and data pipelines using Python (pandas, scikit-learn, PyTorch/TensorFlow), PySpark, and SQL.
• Maintain code quality through Git, code reviews, and automated tests.

4. Cloud & Infrastructure

• Deploy and maintain ML infrastructure across AWS/Azure/GCP.
• Implement IaC using Terraform.
• Build and manage containerized ML workloads using Docker or Kubernetes.

5. CI/CD & Automation

• Create CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI.
• Automate data validation, feature generation, training, and deployment pipelines.

6. Monitoring & Observability

• Configure monitoring for data quality, model drift, inference performance, and model health.
• Integrate model explainability using SHAP or LIME.

7. Feature Engineering & Optimization

• Build and manage definitions in Databricks Feature Store.
• Run distributed training and hyperparameter tuning using frameworks such as Optuna or Ray Tune.

8. Collaboration & Documentation

• Work along with DS, DE, DevOps, and business teams.
• Create detailed documentation for pipelines, processes, and systems.
• Mentor junior engineers and support best practices adoption.

Must-Have Skills

• Databricks (Core)
• MLflow
• End-to-End MLOps
• Python pandas, scikit-learn, PyTorch/TensorFlow
• PySpark
• AWS (SageMaker experience preferred)
• Docker / Kubernetes
• CI/CD Jenkins, GitHub Actions, GitLab CI

Location Preference:

Ghansoli, Navi Mumbai candidates preferred.

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