MLOps Engineer

Job Title:

Location:

Experience:

Notice Period:

MLOps Engineer

Key Responsibilities:

ML Pipeline Design & Automation

• Build and maintain CI/CD & CT (Continuous Training) pipelines for ML models using

  Azure DevOps

  and

  Databricks Asset Bundles

  .
• Automate data preprocessing, training, inference, and retraining workflows for large-scale ML deployments.
• Implement incremental backfills and rolling window retraining for time-series forecasting.

Deployment & Infrastructure

• Design job clusters and compute policies in

  Databricks

  for optimal cost-performance trade-offs.
• Implement multi-environment deployment flows (Dev → QA → Prod) with approvals and rollback strategies.
• Deploy ML models to production with monitoring hooks for performance and drift detection.

Data & Model Governance

• Integrate with

  Unity Catalog

  for secure, compliant data and model storage.
• Set up model versioning, lineage tracking, and reproducibility using

  MLflow

  .
• Establish dataset and feature versioning using tools like

  Databricks Feature Store

  .

Monitoring & Observability

• Implement structured logging for model metrics, system performance, and data quality checks.
• Integrate monitoring tools (e.g.,

  Azure Application Insights

  ) for alerting and dashboards.
• Develop automated retraining triggers based on performance degradation.

Mandatory Skills:

• Cloud:

  Azure (mandatory) – Cloud & DevOps expertise.

• ML Pipeline Automation:

  Azure DevOps / GitHub Actions.

• Databricks:

  Asset Bundles, Unity Catalog, Feature Store.

• Model Registry & Experiment Tracking:

  MLflow (or Weights & Biases).

• Programming & Tools:

  Python (pandas, PySpark, scikit-learn, Prophet, ML/DL frameworks), Bash/PowerShell scripting, Git.

• Testing & Quality:

  Data validation, schema enforcement, model testing, CI/CD quality gates.
• Strong communication and stakeholder management skills.

Good-to-Have Skills:

• Experience with time-series forecasting at scale (Prophet, Sarima, XGBoost).
• Retail demand forecasting or energy sector analytics.
• Feature engineering at scale with distributed systems.

If Intrested. Please submit your CV to Khushboo@Sourcebae.com or share it via WhatsApp at 8827565832

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