Ai/ML Ops Engineer

SRS Infoway; Bengaluru; Hybrid; INR 1,200,000 - 2,000,000; Full time

Job summary

SRS Infoway in Bengaluru is seeking an experienced ML Platform Engineer to build and operate the platform capabilities required to deploy, monitor, govern and continuously improve ML and GenAI solutions.

You will design and maintain CI/CD pipelines for models, prompts and AI services, automate packaging, deployment, rollback and manage registries, feature stores and environments. A Bachelor's degree in CS/Engineering is required, with Azure/Kubernetes certifications preferred.

Qualifications

• Build CI/CD and continuous-training pipelines for models, prompts and AI services.
• Automate packaging, deployment, approval, rollback, retirement and artifact versioning.
• Manage model registries, feature pipelines, environments and repositories.
• Monitor model performance, drift, latency, reliability, cost and data quality.
• Provision cloud and Kubernetes infrastructure through Infrastructure as Code and maintain runbooks and audit trails.

Responsibilities

• Build CI/CD and continuous-training pipelines for models, prompts and AI services.
• Automate packaging, deployment, approval, rollback, retirement and artifact versioning.
• Manage model registries, feature pipelines, environments and repositories.
• Monitor model performance, drift, latency, reliability, cost and data quality.
• Provision cloud and Kubernetes infrastructure through Infrastructure as Code and maintain runbooks and audit trails.

Skills

Azure ML, MLflow & Databricks

CI/CD pipelines

Docker & Kubernetes

Terraform & Python

Observability & governance

Education

Bachelor's degree in Computer Science/Engineering or related field

Job description

Role Purpose

Build and operate the platform capabilities required to deploy, monitor, govern and continuously improve ML and GenAI solutions.

Key Responsibilities

• Build CI/CD and continuous-training pipelines for models, prompts and AI services.
• Automate packaging, deployment, approval, rollback, retirement and artifact versioning.
• Manage model registries, feature pipelines, environments and repositories.
• Monitor model performance, drift, latency, reliability, cost and data quality.
• Provision cloud and Kubernetes infrastructure through Infrastructure as Code and maintain runbooks and audit trails.

Required Skills

• Azure ML, MLflow and Databricks
• Azure DevOps, GitHub Actions or Jenkins
• Docker, Kubernetes, Terraform and Python
• Model registry, feature store and secrets management
• Observability, release governance and incident support

Qualifications

Bachelor's degree in Computer Science, Engineering or related field. Azure, Kubernetes or DevOps certification preferred.

Key Deliverables / Success Measures

• Clear, approved deliverables aligned to squad objectives
• Evidence of quality, security, compliance and governance controls
• Predictable collaboration across business and technology stakeholders
• Measurable improvement in product adoption, delivery flow or operational outcomes