subject:Staff-level Engineering · AI Platforms · Developer Productivity · SRE

tags:aws-bedrock · mcp · terraform · aws-fis · observability-as-code · intern-pipeline

Appa Rao Vadde

AI Platforms · Developer Productivity · Site Reliability Engineering

apparao.vadde@gmail.com · +1-469-662-3373 · linkedin.com/in/aparvvadde · Boston, MA (Remote)

Ref

Staff-level Engineering Seat

AI Platforms · Dev Productivity · SRE

Dear Hiring Team,

Enterprise engineering orgs live or die on two things — the platforms their teams build on, and the operational discipline to keep them running. That is the work I have done at Cox Automotive for the past six years, and it is the lens through which I am evaluating a Staff-level seat on a team where that combination matters.

Over ten years I have shipped production software at every layer of the stack. A six-agent AWS Bedrock orchestration (Strands SDK + A2A protocol) that replaces hours of manual SRE dashboard triage with a single natural-language query across 40+ services. An IDE-native Model Context Protocol (MCP) server attributing P95 latency to the exact files and lines engineers rewrite, with a DNA-tagging correlation pattern linking 50+ event types through a single component identifier. Context Hub — an open-standard plugin marketplace unifying Claude Code and VS Code GitHub Copilot Chat for 100+ engineers across 25+ teams, shipping 197 skills, 3 agents, and 22 slash commands via one install. Copilot Proxy — a production Go gateway translating the full Anthropic Messages API to OpenAI Chat Completions, unlocking Claude Code CLI for a Microsoft-first org with no new Anthropic spend. A 400-component datacenter-to-AWS migration with $180K annual savings and zero production rollbacks. And an AWS FIS-driven chaos-engineering and GameDay program (MTTR 26–45 min) serving 30+ engineering teams, feeding the Production Readiness Review framework and SLO recalibration across the same 40+ microservices.

The through-line is Infrastructure-as-Code, Observability-as-Code, and Constitution-driven SDLC — platforms others build on, documented well enough that the team does not need me to operate them. Evidence-based engineering, human-in-the-loop consensus, and phase-gated governance are not slogans; they are how my changes ship. The same discipline that keeps on-call calm is what keeps AI acceleration from turning into AI chaos.

What makes the profile unusual for a Staff-level seat is how we grow the team around the platforms. On a 4-engineer team we host 2–3 interns every semester — engineering leadership's designated training ground — and over the past two years 8–12 interns have rotated forward into full-time software engineering roles across the broader organization. We run 1–2 enterprise-wide brownbags per quarter on AI coding agents, Observability-as-Code, chaos engineering, and MCP; principal engineers across the org consult the team for deep-dive engineering questions; and the team operates on the no-boundary principle: you build it, you own it, you run it — from design through incident response. Reliability rigor and cross-domain depth, taught hands-on from day one.

I would value thirty minutes to understand what your team needs from this role, and to walk through the multi-agent orchestration, the migration sequencing, the GameDay fault catalog, or the Observability-as-Code module library in whatever depth is useful. My calendar is open.

whoami · staff-level · 2016 → present

APPA RAO VADDE

AI Platforms Developer Productivity Site Reliability Engineering

apparao.vadde@gmail.com +1-469-662-3373 linkedin.com/in/aparvvadde Boston, MA (Remote)

10+

Years

100+

Engineers

400+

Components

$180K

Annual Savings

99.95%

Uptime

80%

Esc Reduction

cat Numbers at a Glance

15 signals · telemetry.live

10+

Years engineering

100+

Engineers on the platforms

25+

Teams adopting

400+

Components migrated

$180K

Annual AWS savings

99.95%

Uptime @ 300% COVID surge

80%

SRE escalations reduced

30+

Teams served by GameDay

40+

Microservices governed

5,000+

Alert policies as code

197

Skills shipped (Context Hub)

50+

Event types correlated (MCP)

85%

Deploy-time reduction

26–45m

MTTR (L200–L300)

8–12

Interns mentored (2 yrs)

cat Impact Highlights --render=cards

top 10 · staff-signal

01
Replaced hours of manual SRE dashboard triage with a single natural-language query across 40+ services — 6-agent AI orchestration on AWS Bedrock (Strands SDK + A2A protocol) delivering benchmark-graded team-health assessments unified across PagerDuty, New Relic, ServiceNow, and Splunk.

02
Collapsed 25-minute multi-dashboard incident triage into a single IDE prompt — SRE Observability MCP (Go Model Context Protocol server) embedding New Relic telemetry inside AI coding agents; designed a DNA-tagging correlation pattern linking 50+ event types via a single component identifier, attributing P95 latency to the exact files and lines to rewrite.

03
Unified Claude Code and VS Code GitHub Copilot Chat for 100+ engineers across 25+ teams — Context Hub, an enterprise plugin marketplace shipping 197 skills, 3 custom agents, and 22 slash commands via one install on the agentskills.io open standard.

04
Unlocked Claude Code CLI for 100+ engineers in a Microsoft-first org — Copilot Proxy, a production Go gateway routing Claude Code CLI traffic through the same GitHub Copilot endpoints already powering VS Code Copilot Chat, JetBrains, and GitHub Copilot CLI installs; full Anthropic Messages API to OpenAI Chat Completions translation preserves SSE streaming, vision, extended thinking, prompt caching, and tool use — no new Anthropic subscription required.

05
Led Chaos Engineering and GameDay program for 30+ engineering teams — designed 10+ AWS FIS-driven, hypothesis-graded fault-injection exercises with a tiered (L200–L400) fault catalog; MTTR 26–45 min, graded readiness assessments fed the Production Readiness Review framework and SLO recalibration across 40+ microservices.

06
80% reduction in SRE escalations via self-service developer platforms and AI-assisted triage.

07
$180K annual AWS savings through cost optimization and resource rightsizing — idle-resource identification and EC2/RDS rightsizing without touching a single workload's SLA.

08
400-component AWS migration with 85% deployment-time reduction, zero production rollbacks — legacy VMware VMs running zipped-artifact deploys behind a monolithic F5 load balancer → containerized, IaC-provisioned AWS workloads.

09
99.95% uptime maintained during 300% COVID-19 traffic surge through scalable monitoring architecture and self-service observability.

10
Mentored 8–12 interns (2–3 per semester over 2 years) on a 4-engineer team — engineering leadership's designated training ground; interns rotate forward from our team into full-time software engineering roles across the broader organization.

cat Professional Summary

staff-level narrative · 10+ years

Senior engineer with 10+ years across AI platforms, developer productivity, cloud, and Site Reliability Engineering (SRE). Ships production multi-agent systems and developer platforms adopted by 100+ engineers across 25+ teams — not prototypes.

🤖

6-agent AWS Bedrock

Strands SDK + A2A orchestration for incident-response and readiness triage across 40+ services

🔍

SRE Observability MCP

IDE-native Model Context Protocol server attributing telemetry to the exact files and lines engineers rewrite

📦

Context Hub

Open-standard plugin marketplace unifying Claude Code and VS Code GitHub Copilot Chat

Led a 400-component AWS migration ($180K annual savings); designed an AWS FIS-driven chaos-engineering and GameDay program serving 30+ engineering teams; established a Constitution-driven SDLC for AI-augmented development; and rotated 8–12 interns into full-time engineering roles over 2 years as engineering leadership's designated training ground.

core.stack PythonGoJavaScriptSQL AWS BedrockTerraformKubernetesGitHub Actions Multi-AgentMCPA2A Claude CodeGitHub Copilot AWS FISNew RelicSplunkPagerDuty

ls Technical Expertise/

13 domains · deep + broad

🔤Core Languages

PythonGoSQLJavaScriptBash

🤖AI & Agents

AWS Bedrock AgentCoreStrands SDKA2A ProtocolMCPMulti-Agent OrchestrationLLM Prompt EngineeringClaude CodeGitHub CopilotReAct PatternHuman-in-the-LoopTelemetry-Grounded Code AttributionLLM Tool Consolidation

📐AI Platform Standards

agentskills.ioClaude Code Plugin MarketplacesVS Code Copilot Chat Agent PluginsSKILL.md FrontmatterSub-agentsHooksSlash Commands

🌉AI Gateway Engineering

Anthropic Messages API → OpenAI Chat CompletionsSSE StreamingOAuth Device FlowToken Lifecycle & Auto-RefreshRate Limit Handling (Exp. Backoff)Zero-Allocation Hot PathsgovulncheckSemantic Release AutomationMulti-Platform Binary Distribution

🧰Developer Experience

Internal Developer Platforms (IDP)Plugin MarketplacesSelf-Service CLIsCross-Platform Go Binaries (stdlib-only)POSIX shell + PowerShell parityLiquid Glass · Fluent · Mica · Acrylic design systems

🧩Frameworks

DjangoReactFastAPIPydanticboto3REST APIsPlaywright

☁️Cloud Platforms

AWS BedrockECSLambdaStep FunctionsRDSDynamoDBS3IAMVPCCloudWatch

🏗️Infrastructure

TerraformHelmDockerKubernetesGitHub ActionsJenkins (Groovy pipeline-as-code)Octopus Deploy (via Terraform provider)

📈Observability

Splunk Enterprise (SPL, ES, ITSI, MLTK)Splunk Observability Cloud (SignalFlow, APM, RUM, Synthetics)New Relic (NRQL, NerdGraph, APM, Browser, Mobile)PagerDutyKubernetes + PixieOpenTelemetryLangfuse (LLM o11y)SLO/SLI DesignIncident Management

🧨Chaos & Resilience

AWS FISHypothesis-Driven Fault InjectionGameDay Program DesignBlast Radius SizingIncident Commander (PagerDuty IR)MTTI/MTTR/MTTACascading Failure AnalysisBreak-Glass & Role-EscalationCircuit Breaker & BulkheadProduction Readiness ReviewsRegion-Failover DrillsPost-Incident Retrospectives

📊Data Engineering

SnowflakePowerBIETL PipelinesData Modeling

⚖️Governance & SDLC

Constitution-driven SDLCPhase-gated ConsensusReAct Scratchpad DisciplineEvidence-Based AssertionsSemVerADRs

🔄Methodologies

Agile/ScrumTDDInfrastructure as CodeObservability as CodeChaos EngineeringGameDay OrchestrationHypothesis-Driven ExperimentationEvidence-Based Engineering

render career.graph --interactive

15 nodes · 4 communities · cross-domain edges

How the work connects. Click any node to jump straight to the exact bullet or card and watch it light up. Hover to trace related work · press Esc or scroll to clear focus.

AI Platform Cloud Migration SRE / Chaos SWE Foundations Bridge / Team hover trace edges · click jump · G focus

cat Professional Experience.log

2 roles · 10+ years · interview-defensible

Senior Software Engineer

Cox Automotive· Boston, MA (Remote)· Feb 2020 → Present

🤖AI Enablement & Developer Productivity

Architected a multi-agent AI system on AWS Bedrock AgentCore using Strands SDK and A2A protocol — 6 autonomous agents (PagerDuty, New Relic, Snowflake ITSM, Splunk, PRR, CloudWatch) orchestrated to assess incident response health, on-call burden, and operational readiness across 40+ services, replacing hours of manual dashboard triage with a single natural-language question.

Unified PagerDuty incident analytics (MTTA/TTE/MTTR), New Relic observability signals, ServiceNow ITSM records, and Production Readiness scores into a Slack-native interface — enabling directors and on-call engineers alike to get benchmark-graded team health assessments without touching a dashboard or writing a query.

Designed and shipped Copilot Proxy — a production Go gateway (<1ms overhead, 200+ concurrent connections) that routes Claude Code CLI traffic through the same GitHub Copilot endpoints our Microsoft-first org already uses from VS Code Copilot Chat, JetBrains, and GitHub Copilot CLI; unlocked Claude Code CLI's plugin, agent, and high-capability system-prompt tooling for 100+ engineers with no new Anthropic spend. ⚠ Claude Code CLI only; Claude Desktop requires a separate claude.ai subscription and is out of scope.

Implemented full Anthropic Messages API to OpenAI Chat Completions translation with real-time SSE streaming, OAuth device flow with auto token refresh, rate-limit handling with exponential backoff, and complete feature parity (vision, extended thinking, prompt caching, tool use, multi-turn context); distributed as signed binaries for macOS/Linux/Windows (arm64 and amd64) via semantic release automation with govulncheck security scanning.

Designed the SRE Observability MCP Server (Go) — a Model Context Protocol gateway making New Relic a first-class tool inside AI coding agents. Designed a DNA-based cross-event correlation pattern (single-tag linking 50+ event types — Transaction, Span, SystemSample, K8sPodSample, PageView — via component CI-ID) and consolidated 69 single-purpose operations into 9 intent-driven tools as a deliberate LLM-context-efficiency pattern. Pairs with coding agents running in parallel to attribute P95 latency to specific files and lines and draft surgical rewrites — replacing 25-minute multi-dashboard triage with a single IDE prompt.

Built complementary MCP connectors for Rally (work management) and a Figma-to-React design pipeline — extending the MCP footprint across the SDLC for 25+ teams.

Launched Context Hub (formerly Skills Hub) — enterprise plugin marketplace and CLI for packaging and distributing reusable AI agent skills across Claude Code and VS Code GitHub Copilot Chat via the agentskills.io open standard. Ships cross-platform binaries, a liquid-glass Pages catalog, and a multi-target plugin dispatcher — one install, two tools.

Shipped the Observability Plugin — 197 skills + 3 custom agents + 22 slash commands covering Splunk Enterprise, Splunk Observability Cloud, and New Relic. ReAct + human-in-the-loop on every write path; docs-grounded, validator-before-write.

Established a Constitution-driven SDLC for AI-augmented development — 6-phase governance with consensus gates at every transition, producing auditable change history and preventing AI-driven scope drift.

Led GitHub Copilot enterprise rollout with security guardrails and SDLC compliance, achieving 98% developer adoption within 3 months.

Designed self-service developer platforms reducing engineering escalations 80% through automated Terraform validation, AWS diagnostics, and bottleneck detection.

Authored Architecture Design Reviews (ADRs) for engineering initiatives, submitting to principals/architects for approval and reviewing peer ADRs across teams.

Co-ran the team's intern mentorship cohorts — 2–3 interns every semester for the past 2 years (8–12 total) on a 4-engineer team; engineering leadership's designated training ground for new engineers, selected for our no-boundary culture and cross-domain depth across AI platforms, cloud, and SRE. Exposed every intern to the full software engineering lifecycle — design, implementation, code review, deployment, and incident response — before they rotate forward to full-time software engineering roles across the broader organization, arriving at their receiving team ready to take on any challenge from day one.

Technical cross-level resource — supported peer engineers through SWE I/II to Senior leveling transitions via code walkthroughs, ADR coaching, and documentation synthesis; principal engineers consult the team for deep-dive engineering questions across AI, cloud, and SRE domains.

Delivered 1–2 enterprise-wide brownbag sessions per quarter on modern engineering tooling and platform work (AWS ECS containerization, AI coding agents, MCP, Observability-as-Code, chaos engineering, developer productivity) — a default team practice of turning internal builds into broader organizational knowledge.

☁️Cloud Migration & Platform Engineering

Led datacenter-to-AWS migration for 400+ components across 5 environments with a 4-engineer team — modernized from legacy VMware VMs running zipped-artifact deploys behind a monolithic F5 load balancer to containerized, IaC-provisioned AWS workloads; delivered on schedule with zero production rollbacks.

Built reusable Terraform modules for multi-account AWS provisioning (5 accounts), reducing deployment time 85% from 3 days to 4 hours.

Designed multi-region DR architecture with automated failover testing, achieving 4-hour RTO vs. datacenter's 48-hour manual process.

Implemented cost optimization identifying idle resources and rightsizing EC2/RDS instances, achieving $180K annual savings (25% reduction).

Refactored 5,000+ lines of legacy application code across 30+ services to 12-factor compliance — externalizing hardcoded configuration, service URLs, and secrets into IaC-fed environment variables — as a prerequisite to containerization; rewrote CI/CD from Jenkins Groovy pipelines to GitHub Actions + Octopus Deploy (all pipeline-as-code via Terraform), reducing build times 40%.

🔧Site Reliability Engineering

Chaos Engineering & GameDay Program Lead — designed and facilitated 10+ hypothesis-driven GameDays for 30+ engineering teams; owned fault catalog design, blast-radius sizing, Incident Commander rotation, and cross-functional coordination across engineering leadership, component SMEs, DBAs, Security/WAF, and AWS break-glass approvers; outputs fed the Production Readiness Review framework and SLO/error-budget recalibration across 40+ microservices.

Designed a tiered fault catalog (L200/L300/L400) executed via AWS Fault Injection Simulator (CPU stress, network latency, packet loss) and deliberate AWS control-plane manipulation — IAM permission strips on SQS, AWS WAF IP-set exclusions on NAT Gateway egress, VPC Network ACL outbound egress denial (region-failover simulation), Security Group port-range tampering on VPC endpoints, Lambda reserved-concurrency throttling (exposing 429-to-API-Gateway-500 masking), ECS task-definition degradation, circuit-breaker route flipping, and Secrets Manager certificate corruption.

Reverse-engineered application code, AWS service configurations (ECS, Lambda, SQS, API Gateway, NACL, WAF, VPC Endpoint, IAM, Secrets Manager), and NRQL/Splunk telemetry to pre-compute failure modes and author ideal-resolution runbooks — delivering MTTR of 26–45 minutes on L200–L300 scenarios; led a severity-1 region-outage drill that drove structural incident-response and checklist reforms.

Authored every post-exercise retrospective — star-rated exercise-effectiveness, team-performance, and process-maturity assessments plus structured action items that fed checklist updates, PRR signals, and SLO recalibrations; grounded in live session note-taking and replay-driven review of recordings.

Resolved 200+ critical incidents as primary escalation point across application code, infrastructure, and observability platforms; pulled into cross-functional warrooms and special projects to unblock high-priority customer-facing production events — partnered with product, support, and engineering to ship fixes at speed and keep dealers productive on our tool suite.

Pioneered Observability-as-Code across 40+ microservices — authored custom Terraform modules where a single YAML input provisions New Relic Key Transactions, Service Level (SLO) calculator dashboards, and Transaction-segment breakdowns covering every Critical User Journey step, giving on-call engineers forensic, span-level visibility into critical flows.

Managed 5,000+ alert policies across New Relic, PagerDuty, and Splunk as code via the same module library, eliminating configuration drift; ran workshops and authored self-paced documentation enabling 30+ teams to self-serve.

Deployed a production-grade New Relic Kubernetes observability stack — Terraform-provisioned clusters with Helm-installed nri-bundle (infrastructure agent, logging, Prometheus OpenMetrics, Pixie) and the standalone newrelic-pixie chart for long-term Pixie data retention — extending Observability-as-Code into the container tier.

Established SLO/SLI frameworks with error-budget tracking across 40+ microservices, reducing feature rollbacks 35%.

Achieved 70% MTTR reduction, 60% alert noise decrease, 45% fewer critical incidents — saving ~800 engineering hours annually.

Created Production Readiness Review framework adopted by 15+ teams, reducing post-handoff incidents 50% and accelerating onboarding from 6 to 3 weeks.

Maintained 99.95% uptime during COVID-19 traffic surge (300% volume increase) through scalable monitoring architecture.

Software Engineer

Cox Automotive· New York, NY· Jul 2016 → Jan 2020

Built dealership financing SaaS platform (Python/Django, React) serving 15,000+ dealerships processing 40M daily transactions.

Engineered lender REST APIs handling 500K credit applications and 125K contract fundings weekly with 99.9% SLA uptime.

Developed digital contracting engine automating compliance across 50 states and 600+ lender rule sets, reducing processing time 60%.

Led enterprise Python 2 to Python 3 migration across the full product suite (40+ services, 15 teams) — owned technical project management across testing, CI/CD updates, and production rollout.

Lived "you build it, you own it, you run it" — owned on-call for services my team shipped, led production-incident triage and resolution, tuned Django ORM / SQLAlchemy hot paths at the 40M-transaction tier, and authored Splunk + New Relic dashboards, alerts, and reports consumed by the team, the enterprise Operations Center, and engineering leadership.

Selected for Special Projects Group (top 2% of engineers) for cross-functional projects and war rooms.

cat Education

2 degrees

M.S. Computer Science

The University of Texas at Dallas

2016

B.S. Information Technology

JNTU-Hyderabad

2012