Platform SRE

YASH Technologies; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

YASH Technologies in Bengaluru seeks a seasoned Platform Reliability Engineer to own end‑to‑end production Kubernetes on AWS, drive performance tuning, and ensure reliability for AI/MCP workloads. You will mentor vendors and stakeholders and shape platform direction.

You will optimise memory usage, container runtimes, and serverless components, implement scalable CI/CD, and lead incident response in a regulated environment.

Qualifications

• 5–10 years in software, DevOps, SRE or production support.
• Working knowledge of GCP and/or Azure; multi-cloud.
• CI/CD with GitHub Actions, Jenkins or GitLab.
• Scripting in Python and/or Bash; YAML.

Responsibilities

• Own production Kubernetes/EKS and AWS infrastructure reliability.
• Tune memory and runtime performance; diagnose issues.
• Deploy and operate LLM-backed AI services in cloud.
• Monitor and troubleshoot platform at scale.

Skills

AWS expertise

Kubernetes (EKS)

Memory tuning

Serverless tuning

AI platform ops

Database ops

Terraform/CF

CI/CD pipelines

SRE/DevOps

Monitoring

Education

Bachelor's degree or equivalent

Tools

Helm

GitHub Actions

Jenkins

GitLab

Terraform

Job description

• Owns reliability and performance of the platform end to end - leads incident response, drives cluster and runtime tuning standards, and sets direction for the AI/MCP platform.
• Expected to mentor and influence vendor and stakeholder decisions.
• Looking for a strong AWS & Kubernetes (EKS) expert to manage production-scale cloud infrastructure, platform reliability, performance tuning, and automation.
• Experience with AI/LLM platform operations, monitoring, troubleshooting, and cloud-native environments is highly preferred.
• This is a deep platform and performance-engineering role - owning production Kubernetes, AWS infrastructure, and the reliability of LLM-backed and agentic services in a regulated environment.
• It goes well beyond standard SRE: we are looking for an engineer who tunes clusters, diagnoses memory and runtime behaviour, and operates AI platform workloads at production scale

Mandatory Skills:

• Deep AWS expertise - End-to-end production deployment on AWS is a must - EC2, ECS, EKS, Lambda, S3, IAM, RDS, API Gateway, VPC, EFS, SNS, SQS, EventBridge, CodeBuild.
• Kubernetes (deep & mandatory) - Production EKS ownership - cluster configuration (CPU/ RAM sizing, node groups, autoscaling), workload fine-tuning (requests/limits, HPA/VPA, eviction policies), and hands-on Helm chart management.
• Memory expertise - Container vs. runtime memory models, OOMKill diagnosis, cgroup behaviour, JVM/Python heap tuning, and database buffer-pool configuration.
• Serverless fine-tuning - Lambda memory/concurrency/cold-start optimization, ECS/Fargate task sizing, and serverless cost-performance tradeoffs.
• Database operations - Running and tuning RDS or equivalent in production; StatefulSet-based DB deployments in K8s a strong plus.
• AI platform & MCP environment - Hands-on deployment or operation of LLM-backed services, MCP servers, or agentic pipelines on cloud infrastructure.

Core Requirements:

• 5-10 years in software development, DevOps, SRE or production support roles.
• Working knowledge of GCP and/or Azure - multi-cloud integrations, platform differences, hybrid workloads.
• CI/CD with GitHub Actions, Jenkins or GitLab.
• Scripting proficiency in Python and/or Bash; YAML fluency.
• Terraform and/or CloudFormation for infrastructure provisioning.
• ITIL framework across Incident, Change, Problem and CAPA management.
• Monitoring with Splunk and/or Grafana - including infra-level resource and memory dashboards.
• ServiceNow and JIRA; strong ITSM discipline.
• Bachelor's degree or equivalent practical experience