mandatory

SN Required Information Details

1 Role SRE (Site Reliability Engineer) - Lead

Engineer

2 Required Technical Skill Set Site Reliability Engineering (SRE),

Google Cloud Platform (GCP),

Kubernetes, Docker, Terraform,

Jenkins, Splunk, Datadog,

Prometheus, Grafana, CI/CD,

Infrastructure as Code (IaC), Incident

Management, Observability, Python,

AI/ML Reliability, MLOps, Vertex AI,

AIOps, LangChain, LangGraph, RAG

Systems

3 No of Requirements 1

4 Desired Experience Range 05-08 years

5 Location of Requirement Chennai/ Pune/Hyderabad

Desired Competencies (Technical/Behavioral Competency)

Must-Have

(Ideally should not be more than 8-10)

 Strong hands-on experience in Site Reliability Engineering (SRE) and modern DevOps practices.

 Experience in Google Cloud Platform (GCP) environment setup, deployment, and operations.

 Strong expertise in Kubernetes and Docker container orchestration.

 Experience developing and managing CI/CD pipelines using Jenkins.

 Strong knowledge of Terraform and Infrastructure as Code (IaC).

 Experience with Splunk and enterprise observability platforms.

 Hands-on experience with Datadog, Prometheus, or Grafana monitoring solutions.

 Experience defining and managing SLIs, SLOs, SLAs, and Error Budgets.

 Strong background in Incident Management, On-call Operations, MTTA/MTTR tracking,

and Blameless Postmortems.

 Experience in Python scripting and automation.

 Understanding of AI/ML platform reliability, MLOps, and model monitoring.

 Knowledge of Vertex AI, AI observability, model drift monitoring, and AIOps concepts.

 Strong analytical, troubleshooting, and root-cause analysis skills.

 Excellent stakeholder communication and leadership skills.

Good-to-Have Behavioral Attributes:





 Ability to lead a reliability workstream independently.

 Robust decision-making and ownership mindset.

 Excellent presentation and communication skills.

 Experience working with Manufacturing Engineering platforms.

 Experience mentoring SRE, DevOps, or platform engineering teams.

 Exposure to Agentic AI, LangChain, LangGraph, and RAG architectures.

 Experience in production-scale AI/ML deployments.

 Familiarity with GitOps and ArgoCD.

 Ability to work in a fast-paced enterprise environment with

CST 2017 JD Ver 2.0

executive visibility.

 Strong customer and business-oriented mindset.

SN Responsibility of / Expectations from the Role

 Define and maintain SLOs, SLAs, SLIs, and Error Budgets for critical Manufacturing Engineering platforms.

 Design and implement enterprise-grade observability solutions across applications and infrastructure.

 Build and maintain monitoring dashboards and actionable alerting mechanisms.

 Develop and support Jenkins-based CI/CD pipelines for reliable and scalable deployments.

 Manage Infrastructure as Code using Terraform across GCP environments.

 Deploy, administer, and optimize Kubernetes-based production platforms.

 Conduct load testing, stress testing, and production readiness assessments.

 Own major incident management processes and on-call operations.

 Develop runbooks, incident response procedures,



and postmortem documentation.

 Implement reliability and observability frameworks for AI/ML applications and Agentic AI systems.

 Monitor AI model performance, model drift, data drift, and inference reliability.

 Implement AIOps capabilities including anomaly detection, predictive alerting, and incident auto-triage.

 Support GenAI-assisted incident response and automated RCA initiatives.

 Collaborate with Manufacturing Engineering, AI/ML Engineering, and Enterprise IT teams.

 Drive automation initiatives to reduce operational toil and improve system reliability.

 Mentor engineering teams on SRE best practices and operational excellence

Details For Candidate Briefing**

(It should NOT contain any confidential information or references)

About Client

Global Auto OEM

Domain: Manufacturing Engineering, Cloud Engineering, Platform Engineering, Site Reliability

Engineering (SRE), AI/ML Engineering, Digital Manufacturing

USP of the Role and Project:

 Opportunity to establish enterprise-scale SRE practices for global manufacturing platforms.

 Lead AI and Agentic AI reliability initiatives in a cutting-edge manufacturing environment.

 Work on Google Cloud Platform, Kubernetes, Terraform, and enterprise observability solutions.

 Exposure to AI/ML Reliability Engineering, MLOps, and AIOps.

 Opportunity to design enterprise monitoring, incident management, and automation frameworks.

 Work closely with global manufacturing engineering and AI teams.

 High visibility role with leadership engagement.

CST 2017 JD Ver 2.0

 Opportunity to shape future-state reliability and automation strategy for Manufacturing

Engineering platforms

📌 Sr Reliability Engineer- Ford (Hyderabad)
🏢 TCS_India
📍 Hyderabad