Open to opportunities

Principal AI architect — production agent systems you can inspect before we talk.

Principal review path: governance (AegisAI) → orchestration (VAP) → access-aware RAG → governed publish (Content Factory) → ADR portfolio. Catalog has 17 products; panels start on the spine. 18 catalogued repos.

Sr. Staff Engineer — Software Architecture & Full-Stack · Lucid Motors

Principal AI Architect · AI Architect · Applied AI Engineer · Forward Deployed Engineer

Interview pack

One path: review → playbook → arena

Panels start on the live spine, then use the playbook and Practice Arena for depth. Check spine-health before the call — on Render Free, first hit after idle can take 15–40s (cold start), not a warm always-on SLO.

Signal

Writing and proof — not launch spam

ADR-first posts and a flagship essay with live spine proof. Calendar and outreach logs live in the architecture portfolio.

Flagship essay

From Multi-Agent OS to Agent Governance — with live proof links.

Substack

Weekly architecture decisions — not product launch spam.

ADR post calendar (S5)

10 decision-first posts mapped to ADRs + technical-review CTAs.

Golden path artifact

Stranger-replayable ask→RAG→govern→meter JSON proof.

NIST AI RMF one-pager

Govern/Map/Measure/Manage controls mapped to live proof links.

FinOps ROI one-pager

Cost-as-constraint narrative with golden-path meter proof.

AWS ephemeral receipts

Control-plane IaC: AegisAI ECS apply→destroy (not always-on EKS).

GCP serverless receipts

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

Cloud free-tier split

AWS vs GCP assignment: what stays warm on Render vs receipt labs.

Panel day (Free interim)

15-min script with cold-start honesty + local/GCP Strict ERAG.

Current Role

Sr. Staff Engineer — Software Architecture & Full-Stack

Lucid Motors · Production AI platforms · since 2023

Lucid MotorsVolvo CarsKaiser PermanenteGoogle

What AI lab & platform recruiting teams evaluate first

Four questions — answered before the first call.

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

Yes — governed multi-agent OS, runtime gateway, access-aware RAG, AgentOps workbench, and content pipeline. Spine platforms wired with traces, evals, and HITL — labs and teaching patterns stay in the full catalog.

Proof: venkat-ai.com/work

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

Yes — 18 GitHub repos, FastAPI + LangGraph + Next.js stack, ADRs, and forward-deployed-style integration work (gateway SDK, OAuth publish, mission consoles). Architecture decisions backed by running code.

Proof: github.com/vpeetla-ai

Do they understand safety, policy, and eval discipline?

Yes — OPA policy gates, human-in-the-loop for side effects, signed audit trails, source coverage scoring, and regression-style eval gates across AegisAI and AegisLoop.

Proof: AegisAI · AegisLoop live platforms

Can we evaluate before scheduling loops?

Yes — 17 catalog products, architecture portfolio with 29 ADRs and case studies, and a forwardable executive brief. Technical review without calendar overhead.

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

Hiring verdict: Strong AI architect and applied AI engineer candidate — production agent systems with eval discipline, hands-on repos, and Google-scale delivery depth. 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 — building production AI platforms while publishing governed agent reference systems teams at AI labs and platform companies can fork and evaluate.

Applied AI scope: multi-agent orchestration (VAP), runtime governance (AegisAI), access-aware RAG, AgentOps missions, and governed content pipelines — each deployed as a live platform with documented trade-offs.

19 years shipping platforms at Google, Kaiser, Volvo, and Lucid — the production engineering foundation that separates applied AI engineers who deploy from researchers who only prototype.

Open-source repos, weekly architecture writing, and inspectable ADRs — so recruiting loops start with technical proof, not credential guessing.

Principal AI Architect

Sets agent system strategy — orchestration, retrieval, tools, policy, evals, and observability — with ADRs, reference implementations, and executive-ready narratives teams can extend.

Proof: Governed AI stack · 29 documented ADRs · 17 catalog products

AI Architect

Designs agent system boundaries — orchestration, retrieval, tools, policy, evals, and observability — with ADRs and reference implementations teams can extend.

Proof: Governed AI stack · 29 documented ADRs · Architecture portfolio on GitHub

Applied AI Engineer

Hands-on builder of LLM products — LangGraph orchestrators, RAG pipelines, gateway integration, eval harnesses, and production deployment on FastAPI + Next.js.

Proof: 18 repos · 17 catalog products · LangGraph + OPA + Qdrant

Forward Deployed Engineer

Ships integrated agent workflows with customer-ready boundaries — gateway SDK, HITL overrides, OAuth adapters, mission consoles, and observable traces for production rollouts.

Proof: AegisAI gateway · Content Factory publish · AegisLoop missions

AI Engineer

Implements agent patterns (ReAct, Reflection, Plan-Execute, Multi-Agent, Swarm) as production units with bounded tools, eval gates, and inspectable trace viewers.

Proof: 5 pattern repos · VAP orchestrators · Live trace platforms

Production agent platforms — governance, orchestration, RAG, AgentOps

Catalogued repos with source and review paths

Architecture decision records — policy, RAG, evals, gateway

6/6

Golden eval suites gating CI merges

19+

Years shipping platforms · incl. Google

Career recommendations

What colleagues say about working with me.

Direct quotes from my public LinkedIn profile. These recommendations validate earlier delivery and collaboration; current AI-architecture authority is evidenced separately through code, ADRs, and 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

Panel Evaluation Fit

How I map to AI architect and applied AI engineering roles.

Production agent architecture
Orchestration + governance + RAG + AgentOps as one wired system — the integration bar most applied AI teams fail before scaling.
Applied AI · Agent infrastructure

Hands-on engineering depth
Not strategy-only — shipped FastAPI services, LangGraph graphs, gateway SDK, eval gates, and full-stack platforms recruiters can click through in minutes.
AI Engineer · Technical screen

Safety & eval discipline
Policy before side effects, HITL for high-risk tools, signed audit, source coverage scoring — production instincts AI labs expect.
Trust & safety · Platform eng

Technical Review

Inspect live systems before scheduling interviews

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

04Content 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

Validate Credentials

Architecture Portfolio

29 ADRs · case studies — trade-offs, outcomes, and live platform links on GitHub.

Live Platforms

5-spine review path first; full catalog of 17 products with diagrams, traces, and repos.

GitHub

18 catalogued ecosystem repos — orchestration, governance, RAG, patterns, AgentOps.

Executive Brief

One-page forwardable summary — print to PDF for hiring managers.

Career history, recommendations, and production delivery track record.

Interview Playbook

System design, cloud architecture, and STAR-method answers grounded in real shipped decisions, not generic prep.

Practice Arena

Mock interviews with dual LLM judges against playbook rubrics.