Description

Lead the design, implementation, and MLOps of production ML pipelines for training and inference.

This role is on-site.

Responsibilities

• Translate data science prototypes into maintainable services with strong testing and observability.
• Implement CI/CD for ML using Azure DevOps or GitHub Actions with Databricks.
• Manage feature engineering workflows, feature stores, and reusable ML components.
• Establish model monitoring for data drift, concept drift, and degradation using MLflow.
• Define guardrails for responsible AI including bias checks, explainability, and auditability.

Required Skills

• 10+ years of experience in software or ML engineering.
• Expert-level Python and SQL.
• Hands-on experience with Spark and DataFrames.
• Proficiency in Git, CI/CD, and Docker.
• Experience with MLOps practices: model versioning, registry, and evaluation pipelines.
• Strong understanding of data modeling and feature engineering fundamentals.

Preferred Skills

• Experience with Azure cloud services, logging, and monitoring.
• Familiarity with Kubernetes and Terraform.
• Exposure to Deep Learning frameworks like TensorFlow or PyToch.

Key Skills

Education

Any Graduate