Virtusa
Website:
virtusa.com
Job details:
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
Click on Apply to know more.
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