MLops Engineer

Key Responsibilities

• Design, develop, and maintain

  end-to-end MLOps pipelines

  for training, testing, and deploying ML models at scale.
• Implement and optimize

  CI/CD workflows using GitHub Actions

  for ML model integration and delivery.
• Work extensively on

  Databricks

  for model training, orchestration, and deployment.
• Automate data ingestion, feature engineering, and ML model retraining workflows.
• Monitor model performance in production and enable automated re-training or alerting mechanisms.
• Experience with code versioning tools like GitHub, Gitlab, SVC, etc.
• Ensure ML systems follow

  best practices in version control, reproducibility, and scalability

  .
• Collaborate with cross-functional teams (Data Scientists, Engineers, Product Managers) to align business objectives with ML solutions.
• Implement security, compliance, and governance standards for ML pipelines.

Must-Have Skills

• Databricks

  (MLflow, Delta Lake, Model Registry, Unity Catalog).

• GitHub CI/CD pipelines

  (bare minimum requirement).
• Proficiency in

  Python

  and ML-related frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong understanding of

  cloud platforms

  (AWS / Azure / GCP) with ML services integration.
• Experience with

  containerization & orchestration

  (Docker, Kubernetes).
• Knowledge of

  data engineering workflows

  and distributed data processing (Spark preferred).

Good-to-Have Skills

• Experience with

  Terraform / Infrastructure as Code (IaC)

  for environment setup.
• Monitoring tools (Prometheus, Grafana, Datadog, or equivalent).
• Knowledge of

  feature stores

  and real-time inference pipelines.
• Exposure to

  data governance, security, and compliance frameworks

  .

Qualifications

• Bachelors or master’s degree in computer science, Data Engineering, or a related field.
• 3–6 years of experience in MLOps, DevOps, or Data Engineering with ML focus.