Description

Lead the architecture, implementation, and operationalization of advanced forecasting models and machine learning systems.

This role is hybrid.

Responsibilities

• Design end-to-end CI/CD pipelines for model training, deployment, monitoring, and retraining.
• Drive infrastructure automation using Terraform, Docker, Kubernetes, and cloud-native services.
• Optimize the cost, performance, and scalability of ML pipelines and runtime environments.
• Partner with data scientists and product owners to translate forecasting requirements into reusable pipelines.
• Mentor junior MLOps engineers and establish internal documentation, templates, and reusable components.

Required Skills

• 10+ years of experience in MLOps, data engineering, or ML infrastructure roles.
• Proficiency in Databricks, MLflow, and Unity Catalog.
• Strong programming skills in Python and PySpark for building ETL flows and ML components.
• Hands-on experience with CI/CD pipelines and GitOps practices.
• Infrastructure-as-code experience using Terraform.
• Experience with containerization using Docker and orchestration via Kubernetes.
• Cloud expertise with AWS (SageMaker) or Azure (Azure ML).
• Experience with Airflow and feature store solutions.
• Proficiency in SQL for data analysis and ETL validation.

Preferred Skills

• Experience with demand forecasting, inventory optimization, or promotion effectiveness in retail.
• Exposure to model explainability and bias detection workflows.

Key Skills

Education

Any Graduate