Job Description

Job Summary

For ML Ops Project

Key Responsibilities

Develop tools, frameworks and custom components to address regulatory needs in machine learning platforms, such as model training, model deployments, model observability, versioning, explainability, security, infrastructure focusing on AI Governance perspective

• Design, Develop, and maintain large scale data and cloud infrastructure required for machine learning projects.
• Working with CI/CD flow where we strive for total automation of bringing code from a developer to production.
• Utilize software engineering to create efficient, scalable solutions for deployment in critical production environments hosted on GCP/Azure.
• Understanding of Large Language Models and Agentic AI (Highly desired).

Skill Requirements

Experience working with open-source technologies like Nvidia Triton Framework, Mongo, TensorRT, k Serve, K-Native, Apache Kafka etc.

• Experience with CI/CD tools like Jenkins or GitHub Actions.
• Experience of working knowledge on Docker, Kubernetes (k8s) and REST API is a must.
• Have solid experience in MLOps practices, developing ML Pipelines, and deploying ML Models to production.
• Have strong background in Python Programming and hands-on experience in GCP, & Azure.

Other Requirements

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