Anirudh Sridharan (Ani)

Built high performing teams from scratch and scaled multi team initiatives across infrastructure operations, observability, and systems reliability.

Architected SLO driven frameworks and predictive reliability practices that balanced delivery velocity with system resilience, establishing scalable automation and incident management standards that reduced operational toil and improved platform stability.

Designing and shipping infrastructure as code, Kubernetes platforms, and backend services; debugging production systems.

I work with Terraform and Helm, CI/CD, and TypeScript/Go to turn runbooks into automated, platformized workflows.

Practicing customer obsessed engineering, using real user journeys to shape reliability, CX, and operational guardrails.

I bridge non technical needs to engineering decisions with clear narratives and decision memos, aligning teams on measurable outcomes.

Architecting and building scalable internal developer platforms with golden paths, self service infrastructure, and CI/CD at scale.

Design platform abstractions that accelerate delivery, enforce guardrails, and reduce cognitive load for product teams.

Defining SLIs/SLOs, capacity planning, and chaos/performance testing to make quiet oncall a first class outcome.

Actionable observability with OpenTelemetry and service mesh visibility, with policy backed SLOs and guardrails.

Leading incident command, tuning escalation policies, and using correlationID tracing to accelerate root cause analysis.

Reduce alert noise, codify communication templates, and turn retrospectives into durable engineering improvements.

Delivering multi region architectures and high throughput telemetry pipelines with four nines availability targets.

I manage large fleets, forecast capacity with data, and design safe failover and graceful degradation strategies.

Leading teams in building production grade AI agents for reliability workflows, MCP servers for observability platforms, and LLM powered incident analysis that turns tribal knowledge into instant insights.

YODA (AI ops agent), secure MCP servers for New Relic, Datadog, and PagerDuty, plus automated RCA that compresses days of investigation into minutes. AI-first observability correlates signals across millions of metrics; autonomous remediation resolves 70%+ of recurring incidents; intelligent capacity planning optimizes spend while ensuring headroom; and ChatOps agents reduce MTTR by 50%+ through automated runbook execution. Led an 8-person observability team adopting AI-native practices, and established guardrails for safe, compliant agent deployment in production. Delivered $3M+ annualized impact through intelligent automation roadmaps.

Using LLMs and ML models to correlate signals across millions of metrics, traces, and logs to surface the few things that actually matter.

Connects telemetry to service ownership, change events, and runbooks so the right engineer sees the right context instantly.

Building self-healing automation that resolves 70%+ of recurring incidents safely and consistently.

Designed for safety: guardrails, approvals, blast-radius controls, and post-action validation.

Forecasting demand and tuning capacity with ML-driven models to reduce waste while protecting availability.

Aligns performance headroom with cost objectives so reliability and efficiency improve together.

Context-aware assistants that reduce MTTR by 50%+ via automated triage, summaries, and runbook execution.

Turns tribal knowledge into action: guided troubleshooting, safe remediations, and compliant incident notes.