Description

Lead the design, implementation, and operational reliability of production machine learning pipelines for training, batch inference, and real-time scoring.

This role is on-site.

Responsibilities

• Translate data science prototypes into robust, maintainable services with strong testing, observability, and reliability.
• Build and manage feature engineering workflows, feature stores, and reusable ML components.
• Implement CI/CD for ML using Azure DevOps or GitHub Actions integrated with Databricks, including model versioning and rollback strategies.
• Establish best practices for model monitoring, including data drift, concept drift, and degradation alerting.
• Provide technical leadership, conduct code reviews, and mentor junior ML engineers.

Required Skills

• 10+ years of software engineering experience with strong proficiency in Python and SQL.
• Experience with Spark, DataFrames, data modeling fundamentals, and feature engineering.
• Proficiency with MLflow (or equivalent) for experiment tracking, model registry, and lifecycle management.
• Hands-on experience with Git, CI/CD pipelines, and Docker.
• Knowledge of Azure cloud services, including logging and monitoring.
• Experience with Kubernetes and Terraform is preferred.
• Strong understanding of MLOps practices, including model packaging, deployment patterns, and performance optimization.

Preferred Skills

• Exposure to deep learning frameworks such as TensorFlow or PyTorch.
• Experience with Java or Scala.

Key Skills

Education

Any Graduate