Description

You will lead the architecture, implementation, and operationalization of machine learning systems and forecasting models.

Responsibilities

• Design end-to-end CI/CD pipelines for model training, deployment, monitoring, and retraining using Databricks, MLflow, Unity Catalog, and Airflow.
• Drive infrastructure automation using Terraform, Docker, Kubernetes, and cloud-native services such as SageMaker or Azure ML.
• Optimize the cost, performance, and scalability of ML pipelines and runtime environments.
• Partner with data scientists and product owners to translate forecasting and planning requirements into reusable pipelines.
• Lead incident resolution and root cause analysis when models fail in production or regress in performance.
• Mentor junior MLOps engineers and establish internal documentation and reusable components.

Required Skills

• 10+ years of experience in MLOps, data engineering, or ML infrastructure roles.
• Proficiency in Python and PySpark for building ETL flows and reusable ML components.
• Deep expertise with Databricks, MLflow, Unity Catalog, and Airflow.
• Hands-on experience with AWS (S3, EC2, EKS, SageMaker) or Azure (ML Studio, Synapse, AKS).
• Strong SQL skills for data analysis and ETL validation using Snowflake or Databricks.
• Experience with CI/CD pipelines, GitOps practices, and Infrastructure-as-Code (Terraform, CloudFormation).
• Proficiency with containerization (Docker) and orchestration (Kubernetes).
• Experience operationalizing ML models for forecasting, planning, or optimization in retail or ecommerce.
• Familiarity with observability tools like Grafana, Prometheus, or ELK.

Preferred Skills

• Experience with demand forecasting, inventory optimization, or promotion effectiveness.
• Exposure to model explainability, bias detection, and data governance.

Key Skills

Education

Any Graduate