Lead MLOps Engineer / Site Reliability Engineer – AI/ML Infrastructure

Virtusa; Hyderabad; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

Virtusa seeks a Lead Site Reliability Engineer to architect and maintain cloud-native MLOps pipelines for scalable AI/ML workflows in the AIDA Engineering group. You will work with AI engineers, data engineers, and software teams to build containerized infrastructure that supports a modern SDLC and ensures production reliability in Hyderabad.

You will apply SRE best practices, incident management, and automation using Python and Terraform across AWS services and Bedrock.

Qualifications

• 10–15 years of software engineering or related field experience with SRE/DevOps background.
• Strong expertise in AWS/GCP cloud platforms and Terraform.
• Proficiency in Python for scripting and automation, plus containerization with Kubernetes.

Responsibilities

• Lead SRE/DevOps efforts for cloud-native AI/ML platforms.
• Architect, implement, and maintain scalable MLOps pipelines in AWS/GCP.
• Manage incidents, triage and resolve high-severity outages with cross-functional teams.

Skills

SRE principles

Troubleshooting

Incident management

Communication

Education

Bachelor's degree (optional)

Tools

Kubernetes

Databricks MLFlow

Terraform

Weaviate

AWS (EC2,S3,Lambda)

Bedrock

Job description

Roles and Responsibilities

Lead (MLOps & Infrastructure Reliability) – P4

We are looking for an intelligent, resourceful, and highly skilled Lead Site Reliability Engineer to

join our AIDA Engineering group. In this role, you’ll focus on architecting and maintaining cloud-

based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows,

working closely with AI engineers, data engineers, and software teams. You will work closely

with application development teams to build scalable, containerized infrastructure to support a

modern SDLC process. This group plays a critical role in ensuring the stability, reliability, and

availability of mission-critical production applications across Wiley.

What we’re looking for:

Up to 5 years of experience in a Site Reliability Engineering (SRE), DevOps, or

Production Engineering role, with a deep understanding of SRE principles and best

practices.

Design, implement, and maintain cloud-native platforms to support AI and data

workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock.

Hands-on experience with Databricks MLFlow, including model registration, versioning,

asset bundles, and model serving workflows.

Build and manage scalable data pipelines to ingest, transform, and serve data for ML

and analytics.

Proficiency in at least one coding language (Python, Go, Rust, or Typescript) for

automation and debugging.

Hands-on experience in Kubernetes (K8s) for managing and orchestrating containerized

applications.

Cloud experience (AWS preferred) with exposure to key services like EC2, S3, Lambda,

and Bedrock– GCP a plus.

Incident management expertise, including triaging, escalation, and resolution of high-

severity outages.

Strong troubleshooting and problem-solving skills, with experience diagnosing complex

production issues.

Excellent communication skills to articulate technical challenges and solutions

effectively.

Preferred skills:

Experience writing, maintaining and contributing to Terraform.

Experience with AI-native Vector databases, ex. Weaviate.

Architecting and managing AI/ML workloads in production using the employing of

Job Requirements

• 10-15 years of experience in software engineering or a related field.
• Strong expertise in AWS/GCP cloud platforms, including Terraform configuration management.
• Proficiency in Python programming language for scripting tasks such as automation testing.