Project Role : Operations Engineer Project Role Description : Support the operations and/or manage delivery for production systems and services based on operational requirements and service agreement. Must have

skills:
Splunk Enterprise Architecture and Design, Event management with AIOPS , Splunk Enterprise Observability & ITSI Good to have

skills:
NA Minimum 5 Year(s) Of Experience Is Required Educational Qualification : 15 years full time education Summary: A Tools & Platforms Site Reliability Engineer (SRE) ensures the reliability, availability, performance, and continuous improvement of the infrastructure engineering tooling estate — spanning observability platforms, infrastructure-as-code tooling, CI/CD pipelines, ITSM platforms, internal developer portals, secret management, and AI-augmented operations tooling. The role applies a software engineering discipline to platform operations — building automated remediation, establishing SLIs and SLOs for tooling platforms, reducing toil through systematic automation, and owning reliability outcomes end to end across the four tooling pillars. At Level 7 / 8, this individual operates at the intersection of platform engineering, SRE practice, and AI operations — not just keeping platforms running but continuously raising their reliability ceiling. A distinctive aspect of this role is ownership of LLMOps reliability — ensuring AI-augmented operations tooling (runbook automation pipelines, agentic ITSM workflows, RAG knowledge bases, and AI alert correlation services) meets production-grade SLOs in a regulated financial services environment. Observability SRE – ELK/Splunk – OpenTelemetry – SLI/SLO/Error Budget IaC & Automation SRE – Terraform – Ansible/Chef – GitHub Actions/ArgoCD – HashiCorp Vault – Policy-as-Code ITSM & DevOps SRE – ServiceNow – xmatters – Backstage IDP – Jira/Confluence – CMDB Reliability AI Ops SRE – LLMOps Reliability – Agentic ITSM SRE – AI Alert Pipeline SRE – RAG Platform SRE – Model Observability Roles &

Responsibilities:
– Own reliability of observability platforms —Splunk— defining and maintaining SLIs, SLOs, and error budgets for metrics pipelines, alerting systems, and dashboard availability across all infrastructure tiers – Engineer auto-remediation for common observability failures — scraper restarts, index rollover failures, ingest pipeline blockages — reducing MTTR and eliminating repetitive manual toil – Implement and govern OpenTelemetry instrumentation standards across the infrastructure estate — ensuring telemetry coverage is comprehensive, consistent, and production-grade – Drive observability-as-code adoption — dashboards, alert rules, SLO definitions,



and recording rules version-controlled and deployed through GitOps pipelines with automated testing – Perform capacity planning and performance analysis for observability platforms — managing cardinality growth, storage retention, query performance, and ingest throughput at scale – Lead blameless post-mortems for observability platform failures — producing structured RCA with systemic preventive actions that address root causes rather than symptoms AI-Augmented Operations SRE – Own reliability of LLMOps pipelines — monitoring model API health (OpenAI, Anthropic Claude, Google Gemini), prompt execution success rates, token consumption, latency SLOs, and cost anomaly alerting for AI-augmented operations tooling – Engineer reliability for agentic ITSM workflows — LangChain, LlamaIndex, CrewAI — including agent execution health, tool call success rates, human-in-the-loop handoff reliability, and automated failure recovery – Build observability for RAG knowledge base platforms — vector database (Pinecone, Weaviate, ChromaDB) availability, retrieval latency SLOs, embedding pipeline health, and index freshness monitoring – Implement AI alert correlation reliability — ensuring LLM-based alert grouping pipelines maintain accuracy and availability SLOs, with fallback to rule-based alerting during AI platform degradation – Define and enforce LLMOps governance frameworks — prompt version control, model evaluation pipelines, output quality monitoring, and FSI compliance controls (audit logging, data residency) for AI operations tooling – Lead blameless post-mortems for AI tooling failures — diagnosing model degradation, hallucination events, pipeline failures, and agent workflow breakdowns with preventive actions that meet FSI audit standards Professional & Technical

Skills:
Certifications
• Terraform Associate or Professional Splunk Professional
• AWS DevOps Engineer Pro or GCP DevOps Engineer HashiCorp Vault Associate
• Certified Kubernetes Administrator (CKA) ITIL Foundation or Practitioner Must-Have Technical Skills
• Observability SRE: Splunk— SLI/SLO/error budget engineering, OpenTelemetry, ELK/Splunk pipeline reliability, and observability-as-code practices
• IaC Reliability: Terraform — state backend health, drift detection automation, module registry SRE, and policy-as-code pipeline reliability across AWS and GCP
• CI/CD SRE: GitHub Actions, ArgoCD — pipeline health monitoring, runner auto-scaling, deployment success rate SLOs, and automated rollback engineering
• Vault Reliability: HA cluster monitoring,



seal/unseal automation, certificate lifecycle management, and lease renewal automation for secrets infrastructure
• LLMOps Reliability: Model API health monitoring (OpenAI, Anthropic, Gemini), prompt execution SLOs, token/cost anomaly alerting, and AI pipeline auto-remediation
• RAG Platform SRE: Vector database availability (Pinecone, Weaviate, ChromaDB), retrieval latency SLOs, embedding pipeline health, and index freshness monitoring
• Automation & Toil Reduction: Python — SRE automation scripting, event-driven remediation, infrastructure SDK integration (boto3, GCP client), and operational workflow engineering
• Incident Management: P1/P2 bridge leadership, blameless post-mortems, structured RCA, error budget reviews, and SLA-governed resolution in FSI environments
• Performance & Capacity: Platform capacity trending, SLO burn rate alerting, cardinality management, and proactive capacity interventions across observability and AI tooling Preferred / Advantageous – Experience with chaos engineering or game day exercises for platform tooling resilience — validating failure modes in observability, CI/CD, or AI pipeline infrastructure – Familiarity with eBPF-based observability (Cilium, Pixie) for deep platform telemetry and service mesh reliability engineering – Exposure to model serving infrastructure — Triton, vLLM, or similar — for AI/ML platform reliability beyond API-based LLM tooling – Background in SRE or platform engineering within financial services or other highly regulated industries Additional Information: – SLOs for all platform pillars — observability, IaC, CI/CD, ITSM, and AI tooling — are consistently met, with error budgets actively managed and reliability improving measurably quarter-on-quarter – Toil across the tooling estate decreases consistently — manual intervention patterns are replaced by automated, observable workflows and the team's time shifts toward reliability engineering rather than repetitive operations – Major platform incidents are managed with explicit ownership, rapid mobilisation, blameless RCA outputs, and systemic fixes that prevent recurrence – LLMOps and AI-augmented operations tooling meets production SLOs — model API failures, agent workflow breakdowns, and RAG pipeline degradation are detected early, remediated automatically where possible, and escalated with full context when not – Engineering teams across Cloud, Network, Security, Database, and Voice towers rely on platform tooling that is observable, self-healing, and consistently available — the Tools & Platforms SRE is the reason it stays that way
• The candidate should have minimum 5 years of experience in Splunk Enterprise Architecture and Design.
• A 15 years full time education is required.

📌 Operations Engineer (Bengaluru)
🏢 Accenture in India
📍 Bengaluru