Description

You will lead the design and implementation of production ML pipelines for training, batch inference, and real-time scoring. You translate data science prototypes into maintainable services with strong testing, observability, and reliability. You own the MLOps lifecycle, including CI/CD for ML using Azure DevOps or GitHub Actions integrated with Databricks. You establish model monitoring for drift and degradation and enforce guardrails for responsible AI. You provide technical leadership and mentorship to ML engineers, conducting design and code reviews to set engineering standards.

Responsibilities

• Design and implement production ML pipelines for training, batch inference, and near-real-time scoring.
• Build and manage feature engineering workflows, feature stores, and reusable ML components.
• Drive model packaging and deployment using containers, serverless, or managed endpoints.
• Implement CI/CD for ML, including model versioning, automated testing, and rollback strategies.
• Establish model monitoring for data drift, concept drift, and degradation with alerting.

Required Skills

• 10+ years of software engineering experience with strong proficiency in Python.
• Experience with Java or Scala for building robust services.
• Deep knowledge of MLOps practices, including MLflow, model registry, and experiment tracking.
• Proficiency with Docker and Kubernetes for containerized ML deployments.
• Experience with CI/CD tools such as Azure DevOps or GitHub Actions.
• Strong SQL skills and understanding of data modeling fundamentals.
• Familiarity with Spark and DataFrames for large-scale data processing.

Preferred Skills

• Experience with Terraform for infrastructure as code.
• Exposure to deep learning frameworks such as TensorFlow or PyTorch.

Key Skills

Education

Any Gradute