Description

You lead the design and implementation of production ML pipelines, owning the MLOps lifecycle from training to real-time scoring.

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