Description

Lead the design and implementation of production ML 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 and observability.
• Build and manage feature engineering workflows and reusable ML components.
• Implement CI/CD for ML pipelines using Azure DevOps or GitHub Actions with Databricks.
• Leverage MLflow for experiment tracking, model registry, and lifecycle management.
• Establish best practices for model monitoring including data drift, concept drift, and alerting.

Required Skills

• 5+ years of experience in ML engineering.
• Strong proficiency in Python and SQL.
• Experience with MLOps practices: model versioning, monitoring, and evaluation pipelines.
• Hands-on experience with MLflow or equivalent tools.
• Proficiency in Git, CI/CD, and Docker.
• Experience with Azure cloud services and logging/monitoring.
• Familiarity with Spark, DataFrames, and data modeling fundamentals.
• Bachelor's degree in a relevant field.

Preferred Skills

• Experience with Java or Scala.
• Knowledge of Kubernetes and Terraform.
• Exposure to deep learning frameworks such as TensorFlow or PyTorch.

Key Skills

Education

Bachelor's degree