MLOps Engineer - Production Support

Role: MLOps Engineer Production Support (Databricks & Python):

Candidates with strong Python + Databricks + CI/CD + Production MLOps support experience who have worked on monitoring, troubleshooting, deployment, and model/data drift management in production ML environments.

Experience Range: 6 to 8 years.

Shift: 1 PM 10 PM IST (Monday to Friday).

Target onboarding: Immediate.

Number of positions: 01.

Location: PAN India with preference at Bangalore.

Key Responsibilities:

- Monitor end-to-end pipeline execution and ensure smooth daily runs.

- Identify, debug, fix issues, and rerun pipelines during failures.

- Collaborate cross-functionally to resolve dependencies and blockers.

- Implement preventive health checks and robust logging for proactive issue detection.

- Design and maintain dashboards for data validation and quality checks.

- Perform data validation and ensure integrity across pipeline outputs.

- Analyze model performance using statistical metrics and monitor data/feature/concept drift.

- Build automated alerts/notifications for pipeline failures.

- Develop agent-based solutions for automated monitoring and debugging.

- Enhance and maintain CI/CD pipelines, including implementing data-based authentication and building new CI/CD workflows.

- Ensure proactiveness & timely deliverables with strong communication, ownership, and stakeholder updates.

- Apply model explainability techniques and leverage GenAI for insights and summarization.

Technical Requirements:

1. Python:

- Strong Python skills & solid understanding of OOP (including inheritance).

- Hands-on experience with unit testing, regression testing, and testing frameworks such as pytest.

- Experience with pandas and PySpark etc.

- Knowledge of software design patterns.

2. Databricks:

- Understanding of Databricks architecture and components.

- Experience with Databricks Asset Bundles.

- Ability to build, enhance, and debug Databricks Jobs/Workflows.

3. CI/CD & Git:

- Knowledge of Git flows, branching strategies, and version control best practices.

- Experience with GitHub Actions.

- Ability to deploy Databricks (dbx) jobs through CI/CD pipelines.

- Familiarity with Azure services, including Key Vault.

4. Data/Model Monitoring:

- Understanding of data drift, model drift, and concept drift.

- Ability to use drift indicators for identifying early-stage data quality issues.

- Experience with monitoring and alerting tools is a plus.

(ref:hirist.tech)