Venkata Peetla

Principal AI Architect

Open reference control plane — not a multi-product company. Principal+ means seams (where orchestration stops and governance starts) plus the org that has to live with them. Embed (discovery → wedge → HITL → handoff) is how that architecture lands — a method, not a second brand. Start on the 6-spine path, then /leadership or /fde. Catalog (18 products / 17 repos) is secondary.

Sr. Staff Engineer — Software Architecture & Full-Stack · Lucid Motors · Principal / Distinguished AI Architect · Head of AI platform

Executive brief LinkedIn Leadership Embed method

Review path

One path: review → leadership → embed

Start on the live spine, then how I run the org. Embed is the landing method. Check /spine-health before the call — Render Free first hit after idle can take 15–40s.

Signal

Writing and proof — not launch spam

Decision-first posts and one flagship essay tied to the live spine. Calendars and outreach logs stay in the architecture portfolio — not here as marketing wallpaper.

FDE Field Method

Discovery → wedge → HITL → handoff — how the architecture lands in the customer org.

Flagship essay

Why I split multi-agent OS from governance — with live proof, not a manifesto.

Substack

Weekly decisions I'd defend in a panel — not launch spam.

ADR post calendar (S5)

Ten decision-first posts tied to ADRs and the review path.

Golden path artifact

Replayable ask→RAG→govern→meter JSON — stranger can verify.

NIST AI RMF one-pager

Govern/Map/Measure/Manage mapped to controls I actually run.

FinOps ROI one-pager

Cost as a constraint, with meter proof from the golden path.

AWS ephemeral receipts

AegisAI ECS apply→destroy. I refuse always-on EKS for portfolio theater.

GCP serverless receipts

Cloud Run FinOps/ERAG — scale-to-zero, Always Free–friendly.

Cloud free-tier split

What stays warm on Render vs what I prove with ephemeral receipts.

Panel day (Free interim)

15-min script: cold-start honesty + local/GCP Strict ERAG when Render isn't enough.

Current Role

Sr. Staff Engineer — Software Architecture & Full-Stack

Lucid Motors · Production AI platforms · since 2023

Lucid MotorsVolvo CarsKaiser PermanenteSparity (Apple / Google clients)

What AI lab & platform recruiting teams evaluate first

Four questions — answered before the first call.

Can they ship production agent systems end-to-end?

I'd keep orchestration and governance as separate layers — I refused merging them into one graph. The spine you can click: gateway + HITL, multi-agent OS, access-aware RAG, governed publish. Spine demos show traces, evals, HITL, and model-plane receipts — labs stay out of the 15-minute path.

Proof: venkat-ai.com/work

Are they hands-on — code, architecture, and customer-ready delivery?

The ADRs aren't a slide deck. Gateway SDK, OAuth publish, mission consoles — running code. When the wedge has to land under SSO and data reality, I embed. 17 repos, FastAPI + LangGraph + Next.js.

Proof: github.com/vpeetla-ai

Can they run the org, not just the graph?

I'd write decision rights, ARB invariants, and a 90-day charter with non-goals in the same doc. Success is a standard another team reuses — not me as the permanent approval bottleneck.

Proof: venkat-ai.com/leadership

Do they understand safety, policy, and eval discipline?

Policy before side effects. HITL on anything irreversible. Signed audit. Decline-to-answer when coverage is thin. Eval gates in CI — not a slide about evals.

Proof: AegisAI · AegisLoop live platforms

Can we evaluate before scheduling loops?

Take the 15-minute spine path, skim 37 ADRs, forward the executive brief. You shouldn't need a calendar invite to decide if the conversation is worth it.

Proof: venkat-ai.com/hire · ai-architecture-portfolio

Hiring verdict: Principal+ architecture and leadership — inspectable control plane, written operating model, eval discipline. Apple/Google work was Sparity client engagement. Recommend technical review via live platforms before panel interview.

Forward executive brief

Audience

Production agent platforms with HITL, RAG, OPA policy, and AgentOps eval discipline — hover acronyms for plain-English definitions.

Sr. Staff Engineer — Software Architecture & Full-Stack at Lucid Motors. I design the AI control plane and sit with the people who have to live with it until the wedge runs — then I leave a team that can operate without me. Public reference systems are forkable so you can judge before we talk.

Principal+ means seams and embed. I'd own where orchestration stops and governance starts, then sit with the people who have to live with it until the wedge runs. Model-plane receipts (buy vs RAG vs PEFT, real L4 runs) sit under that job — not a third seat.

What I refuse: demo theater, soft multi-tenancy, governance bolted into the agent graph, and RAG that ranks before it filters. Public AegisAI is personal work — the control-plane pattern you can inspect.

19 years at Kaiser, Volvo, and Lucid. Apple and Google work was client engagement via Sparity — say it that way. That scar tissue is why I won't ship a chat demo and call it a platform.

Start with /technical-review and /leadership. Catalog tours waste the first fifteen minutes.

Principal AI Architect

I'd own the seams: where orchestration stops, where the gateway starts, how RAG fails closed, which evals gate a release. Model plane is part of that job — buy vs RAG vs PEFT, real L4 receipts, not a second brand. The public spine is personal work you can inspect.

Proof: Governed AI stack · 37 documented ADRs · live spine

Head of AI platform / Distinguished

I'd set decision rights, ARB invariants, and what the org is not allowed to automate. Architecture without the operating model dies in the second quarter — leadership is the other half of Principal+.

Proof: /leadership · 90-day charter · ARB refusals

Embed method (FDE / Applied)

How architecture lands: discovery → scored ≤90-day wedge under their SSO and data → HITL on irreversible writes → handoff. A method on this seat — not a second career brand. I don't invent customer logos.

Proof: FDE field method (/fde) · AegisAI HITL · Enterprise RAG

AI Architect / Principal Engineer

Same boundary work when the org titles the seat Engineer — seams for orchestration, retrieval, tools, policy, and evals, with ADRs a team can extend without rewriting the platform.

Proof: Governed AI stack · 37 documented ADRs

Production agent platforms — governance, orchestration, RAG, AgentOps

Catalogued repos with source and review paths

Architecture decision records — policy, RAG, evals, gateway

Golden-eval suite kinds with real CI gates

19+

Years shipping platforms

Panel Evaluation Fit

What a serious panel should probe.

Production agent architecture
Can they keep orchestration and governance as separate layers and still wire a system that survives contact with identity, tools, and evals? Most teams fail that integration bar before they scale.
Principal Architect · Agent infrastructure

Operating model & succession
Do they write decision rights and non-goals, or do they become the bottleneck they were hired to remove? Day-90 test: another team reused a standard.
VP Eng · Head of AI

Embed method
Do they discover → score a thin wedge → integrate under SSO/data constraints → HITL the scary writes → hand off ownership? How architecture lands — not a second career brand.
Applied AI · Customer engineering

Hands-on engineering depth
Is there running code — FastAPI, LangGraph, gateway SDK, eval gates — or only architecture theater? Click the demos; cold starts are labeled.
AI Engineer · Technical screen

Safety & eval discipline
Policy before side effects. HITL on irreversible tools. Signed audit. Decline when sources are thin. Eval in CI, not in the appendix.
Trust & safety · Platform eng

Model plane honesty
Can they decide buy vs RAG vs PEFT vs self-host and show a receipt that matches the claim — without calling a training run a win-rate or a Free-tier demo a GPU fleet?
Principal · CAIO grill

Technical Review

Click the spine before you book a loop

Full portfolio

01Agent governanceGatewayHITLPolicy

AegisAI

Runtime control plane for agent fleets — gateway, policy, HITL, and audit.

Monitor → Govern → Remediate — a runtime control plane for tool authorization, policy violations, HITL approvals, signed audit, and agent registry lifecycle.

Decision

Control plane over agent builder — enterprises need governance across fleets, not another chat UI.

Signal

Website deploy tools forced through approval_required policy on live platform.

Limitation

Registry defaults to in-memory on free tier; Postgres path documented for production.

What shipped: AegisAI: gateway-first governance control plane →

AI Gateway SDK with OPA policy evaluation
Human-in-the-loop for high-risk tool calls
Agent registry with in-memory persistence (Postgres planned)
Governed orchestrators: content pipeline, stock research

Open live platform GitHub Deep dive

Architecture diagram

Runtime governance control planeArchitecture

02Multi-agent OSLangGraph16 IntentsRAG Lab

Venkat AI Platform

Multi-agent OS with three orchestrators, seven RAG strategies, and gateway-wrapped delivery.

Three LangGraph orchestrators, seven RAG strategies, loop patterns (ReAct · Reflection · Plan-Execute), and gateway-wrapped delivery to Slack, Telegram, and WhatsApp.

Decision

LangGraph stateful graphs over linear chains — enterprise workflows need checkpoints and HITL.

Signal

16 routed intents · 7 retrieval strategies · AegisAI gateway on notify channels.

Limitation

Vector store optional on free tier; Enterprise RAG adapter bridges to governed RAG platform.

What shipped: Venkat AI Platform: three LangGraph orchestrators live →

Platform · Deep Research · Architecture Review orchestrators
Enterprise RAG adapter as 7th retrieval strategy
AegisAI gateway on notify channels
Specialist agents: Web, Knowledge, Critic, Planner

Open live platform GitHub Deep dive

Architecture diagram

Multi-agent reference topologyArchitecture

03Knowledge layerHybrid RAGAccess ControlGraph Expansion

Enterprise RAG Platform

Access-aware hybrid RAG with ingest, citations, and optional HITL gates.

Authorization before ranking, hybrid retrieval, cross-encoder rerank, decline-to-answer, citation traceability, AegisAI HITL bridge, and Langfuse trace export.

Decision

Authorization before ranking — vector DB is implementation; access control is architecture.

Signal

golden-eval-registry CI · adversarial suite · JWT Principal when PRODUCTION_STRICT=1.

Limitation

Default live demo is Demo mode (body Principal) with sticky banner; Strict/JWT is the Principal review path (ADR-0006). Free-tier corpus re-ingests after cold start.

What shipped: Golden eval registry gates real CI builds →

Access-aware filtering before semantic ranking
Hybrid lexical + semantic retrieval with cross-encoder rerank
Decline-to-answer when retrieval confidence is low
JWT-verified Principal under PRODUCTION_STRICT · adversarial golden suite

Open live platform GitHub Deep dive

Architecture diagram

Access-aware RAG pipelineArchitecture

04Model PlaneSLMPEFTvLLM

ModelForge

Model Plane control surface — SLM, PEFT, CUDA vLLM, LLMOps — peer to the agent spine.

Hire-facing Model Plane for SLM bake-offs, PEFT receipts, CUDA vLLM metrics, and LLM gateway enforce+record. Composes DomainForge + upstream vLLM + aegis-llm-gateway (ADR-034).

Decision

One ModelForge flagship beats burying PEFT/vLLM in a teaching drawer (ADR-034).

Signal

Live https://modelforge-gamma.vercel.app/api/v1/posture — PEFT + CUDA vLLM + SLM + gateway all ready; both peft_gpu.json and vllm_cuda.json are real Mistral-7B-Instruct-v0.3 receipts on a rented L4 — real QLoRA SFT + DPO training (peft_gpu.json) and real upstream vLLM serving (vllm_cuda.json, 13.74 tok/s, TTFT p50 371.67ms).

Limitation

PEFT receipt reports real training config/timing, not a quality/win-rate score — DomainForge's S0-S4 eval harness isn't wired to real adapter inference yet (see the receipt's own known_gaps). vLLM metrics are a single dated benchmark run, not an always-on production serve claim.

Honest /api/v1/posture (ready vs smoke vs planned)
Receipt gallery for PEFT · CUDA vLLM · SLM bake-off
Taxonomy glassbox — LoRA · QLoRA · Multi-LoRA · classical ML lane
Composes DomainForge train + gateway route + vLLM serve path
Panel scripts for buy vs RAG vs PEFT vs self-host

Open live platform GitHub Deep dive

Architecture diagram

Model Plane — buy vs RAG vs PEFT · CUDA receipts · gateway enforce+recordArchitecture

05Content automationLangGraphHITLPublish

AI Content Factory

Governed content pipeline — research, multi-platform drafts, HITL, then publish.

Research → five platform drafts → HITL review → governed publish through AegisAI gateway, OAuth adapters, and scheduled cron pipelines.

Decision

AegisAI gateway blocks publish until policy allows — side effects never bypass governance.

Signal

Real LinkedIn/X OAuth publish path when tokens configured.

Limitation

Clerk keys required for full pipeline; invite-only signup in production config.

What shipped: AI Content Factory: governed multi-platform publish →

End-to-end research and multi-platform draft generation
Clerk auth with human approval gates before publish
LinkedIn and X OAuth when tokens configured
AegisAI gateway blocks publish until policy allows

Open live platform GitHub Deep dive

Architecture diagram

Governed content publish flowArchitecture

Career recommendations

What colleagues say about working with me.

Direct quotes from my public LinkedIn profile — they speak to earlier delivery and collaboration. Current AI-architecture chops show up separately: code, ADRs, review paths.

View all on LinkedIn

“Venkata is a dedicated and talented engineer. He was an important team member in building the new iOS app for MAGNIFI. Venkata worked through fast paced and demanding development cycles without losing momentum; committed to helping keep important product releases on schedule. Venkata is team player and always upbeat and collaborative in his approach to development. I'm excited to see what Venkata will do next and look forward to working with him again in the future.”
Mark Koerner
Former colleague · MAGNIFI iOS program
Worked directly together
LinkedIn profile

“I have enjoyed working with Venkata on the MAGNIFI project. He was hired and immediately jumped into the fire of our first iOS release. He handled it with poise and great skill. He was the sole iOS developer on this project and the release was a success. He continues to work with great skill at building our updates. He was instrumental in helping me optimize my iOS emulation for setting up Appium and Selenium automation framework. I would recommend Venkata for an iOS Developer position.”
Former colleague
MAGNIFI iOS program · QA & automation
Worked directly together

Validate Credentials

Architecture Portfolio

37 ADRs and case studies — what I decided, what I refused, and live links.

Live Platforms

Start on the 6-spine path (incl. ModelForge). Full catalog (18) is for after you're convinced the spine holds.

GitHub

17 catalogued ecosystem repos — Principal+ proof you can inspect.

Executive Brief

One page you can forward — print to PDF if that's how your loop works.

Employment and recommendations — verify Lucid / Volvo / Kaiser there first.

Practice Arena

Mock interviews judged against Staff+/Principal rubrics.