Job Summary

This role is responsible for supporting the development and maintenance of machine learning operations workflows, ensuring reliable deployment, monitoring, and automation of ML models and pipelines. The individual applies foundational ML Ops and DevOps skills to deliver assigned tasks efficiently, contributing to the stability and scalability of production ML systems.

Key Responsibilities

1. Maintain and troubleshoot ML pipelines using Python and MLflow, resolving bugs and supporting enhancements based on business requirements.
2. Support deployment automation of ML models utilizing Jenkins, GitLab CI/CD, and AWS CloudFormation, ensuring consistent and reliable delivery.
3. Monitor ML workflows and infrastructure using Prometheus and Grafana, identifying and escalating operational issues for timely resolution.
4. Participate in documentation of ML pipeline enhancements and operational procedures, aligning with CMMi and client standards.
5. Collaborate within the team to gather requirements and present data using ELK Stack and Fluentd, supporting day-to-day operational needs.
6. Assist in version control and code management using Git and GitHub, contributing to the maintenance of ML Ops repositories.

Skill Requirements

5. Production Support Automation

AI / GenAI / Agentic AI

Incident Management

Problem Management

Event Correlation

Self-Healing Frameworks

Auto Remediation

Service Reliability Automation

Strong CI/CD and Production Operations background

Other Requirements

1. Optional but valuable:
2. AWS Certified DevOps Engineer � Associate
3. - Certified Kubernetes Administrator (CKA