Description

You will lead MLOps and AI/ML engineering teams, define standards for ML system design, and manage the full lifecycle of production ML systems. This role is on-site.

Responsibilities

• Lead and mentor MLOps and AI/ML engineers while defining engineering standards for ML system design, deployment, and operations.
• Develop and optimize supervised, unsupervised, and deep learning workflows using PySpark, MLlib, and PyTorch.
• Design and deploy ML solutions on AWS using EKS, EC2, SageMaker, Lambda, and S3 for orchestration and runtime execution.
• Automate cloud provisioning and configuration using Terraform and Infrastructure as Code (IaC) best practices.
• Build model-serving APIs with FastAPI and implement CI/CD pipelines for ML models using GitHub Actions or Jenkins.

Required Skills

• 8-12 years of experience in ML engineering, MLOps, or related roles.
• Strong proficiency in Machine Learning, AI/ML, and DevOps practices for production systems.
• Expertise in PySpark and MLlib for scalable data processing and model development.
• Deep knowledge of AWS services including EKS, EC2, SageMaker, Lambda, and S3.
• Experience building FastAPI-based APIs and CI/CD pipelines using GitHub Actions or Jenkins.
• Proficiency in Terraform and Infrastructure as Code (IaC).

Preferred Skills

• Additional depth across AWS services and deployment patterns.

Key Skills

Education

Any Graduate