Engineering Leader · Technical Co-Founder
Carson Rodrigues
I'm a product engineer — I architect and own features end-to-end. Production AI (voice agents, LLM pipelines), full-stack platforms, and mobile apps — and research papers on the systems I build (fourteen in total — one accepted at ICANN 2026, seven under peer review).
Impact at a glance
1st place · 18 August 2026
Won Adaption Labs's AutoScientist Challenge in Data & Visualization
PolyChart scores how much a chart lies, giving every distortion an exact Tufte Lie Factor rather than a yes/no label. Built on a controlled study of 176 fine-tuning jobs across 13 datasets, driven through a headless client I wrote before the platform's official API shipped. Entered in all seven categories; placed first in this one.
Featured · funded · international · awarded
Research
Seventeen papers on the systems I ship
Voice AI latency, intent detection, MCP architecture, multi-agent reliability — measured on production workloads, not benchmarks. One accepted at ICANN 2026, 9 more under peer review.
Accepted at ICANN 2026 (peer-reviewed, Springer LNCS) — “Latency Optimization for Voice AI,” −41.8% end-to-end latency.
Latency Optimization for a Production Voice AI Platform
A systems-level latency study of a production voice-AI platform (Anthropic Claude intent detection + ElevenLabs TTS over a NestJS WebSocket pipeline). The central finding is that running intent detection and TTS concurrently — rather than shaving any single stage — is the highest-leverage optimization, cutting median end-to-end latency from 3,277 ms to 1,909 ms.
MCP Server Architecture Patterns
A pattern catalogue for production Model Context Protocol (MCP) servers — a Gamma-format taxonomy, anti-patterns, and cross-cutting concerns — validated with a real inter-rater reliability study (kappa = 0.76). Proposes a scoped Proxy-Aggregator pattern once tool counts exceed a practical threshold.
When Do LLMs Replace Fine-Tuned NLU? A Decision Framework for Intent Detection
A decision framework for choosing between LLM classifiers and fine-tuned NLU on noisy production transcripts. Shows that full-data TF-IDF still reaches 95.2% on ATIS, and maps the regimes where an LLM-based intent classifier is — and is not — worth its cost and latency.
When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study
A budget-matched, multi-seed study of whether an LLM advisor actually helps hyperparameter optimization on tabular data. The deflationary finding: the advisor's strong first guess is not an LLM output but a fixed default configuration evaluated before any model call. Once classical search (random, Optuna-TPE, Bayesian optimization, successive halving) is seeded with that same default, the apparent lead collapses within a handful of evaluations and the LLM adds no measurable generalization benefit.
What I do
Four archetypes I get hired for
Voice AI, LLMOps, distributed backends, and engineering leadership — usually two or three at the same time.
Real-time voice AI
What it covers (6)
End-to-end voice agents and avatars: ASR, intent, TTS, transport, latency optimization. Production-grade across 40k+ locations.
End-to-end voice agents and avatars: ASR, intent, TTS, transport, latency optimization. Production-grade across 40k+ locations.
→ −41.8% E2E latency · paper
LLM pipelines & MCP
What it covers (6)
RAG, agent orchestration, evals, MCP server design. Anthropic SDK + multi-model routing. Author of MCP Server Architecture paper.
RAG, agent orchestration, evals, MCP server design. Anthropic SDK + multi-model routing. Author of MCP Server Architecture paper.
→ MCP architecture paper · 5+ AI systems shipped
Distributed backends
What it covers (6)
Event-driven NestJS / FastAPI / Hono backends, geospatial pipelines, queues, observability. The plumbing that keeps AI products up.
Event-driven NestJS / FastAPI / Hono backends, geospatial pipelines, queues, observability. The plumbing that keeps AI products up.
→ 40,000+ locations · 99.9% uptime
Engineering leadership
What it covers (6)
Lead 6+ engineers through architecture reviews, hiring, and shipping cadence. Cut release cycles weeks → hours via infra automation.
Lead 6+ engineers through architecture reviews, hiring, and shipping cadence. Cut release cycles weeks → hours via infra automation.
→ Team of 6 at Ôdasie · founder × 2
Speaking
On stage at BITS Law, Mumbai
3-hour AI for Marketing Masterclass — creative AI tooling, content + distribution automation, zero-code CRM pipelines, hands-on with LLMs.
Featured
Production systems shipped
- Real-time AI voice mock interviews — Pipecat + LiveKit + Claude + ElevenLabs + Deepgram
- LLM-driven resume tailoring per JD with RAG over candidate documents
- Job matching, application tracker, interview prep, upskilling paths
- Resume parsing pipeline: Tesseract OCR + pdf-parse + mammoth
- Multi-payment integration: Razorpay, Stripe, Apple Pay
- 447 of the repo's 2,688 merged PRs are mine; most commits of any contributor
- Admissions CRM with an AI voice agent that calls new enquiries in five minutes (~220k LoC)
- IPE: AI grading of handwritten answer sheets, 12,847 marks reviewed by faculty in prod
- Hono API (152 route modules), Drizzle over Postgres — 323 tables, 216 migrations
- 125,299 questions in the bank, 78% AI-drafted, none published without faculty approval
AI Voice Caddie ft. Andy North (2× US Open Champion)
Senior Software Developer · VoiceQube
- Real-time voice caddie via LiveKit + WebRTC stack
- MongoDB geospatial indexing for live GPS course position
- AWS Lambda + MediaConvert for shot video capture and processing
- Production-grade across 40,000+ golf courses (event-driven backend)
- Voice + intent stack with Anthropic Claude in the loop
- Architected end-to-end across iOS, Android, web, vendor ecosystem
- ML-powered planning workflows and intelligent vendor matching
- Viral QR photo-sharing for live event capture
- Live on App Store and Google Play; multi-market customer base
- Established engineering culture, CI/CD automation, technical roadmap
Mobile apps
Live on the App Store and Play Store
React Native / Expo across health-tech and consumer products. Multi-market customers downloading them today.
CELABE
AI-Powered Wedding & Events Platform
Co-founded multi-market wedding / events platform. iOS, Android, web; ML-powered planning, vendor marketplace, viral QR photo-sharing. Founding engineering leader.
SmokeMukti
ICMR-Funded Tobacco Cessation App
Behavior-change app for tobacco cessation funded by the Indian Council of Medical Research (ICMR). Personalized AI chat coach, progress tracking, goal setting, and 24/7 motivation pathway. Built under guidance of Dr. Anil V. Ankola; ICMR STS-backed research project.
Experience
Where I've shipped
- Co-founded and architected an AI-powered wedding / events platform
- Led full lifecycle from concept to production launch (iOS, Android, web, vendor ecosystem)
- ML-powered planning workflows, viral QR photo-sharing, real-time vendor coordination
- Established engineering culture, CI/CD automation, and the technical roadmap
- Led a team of 6 engineers through architecture reviews, code reviews, and technical mentorship
- Architected and shipped 5+ production AI systems: LLM pipelines, voice agents, MCP servers, geospatial processors
- Real-time AI avatar with LiveKit + WebRTC (bi-directional voice + video + data)
- Heavy n8n automation — LLM agent orchestration, API integrations, approval workflows
- Adopted Claude Code in production engineering workflow; cut release cycles weeks → hours
- Shipped three production AI products end-to-end: Phiny.ai, Crosslane, Northway Tech
- Architected NestJS + AWS backend serving real-time interactions across 40,000+ locations
- Live AI avatars and voice agents — LiveKit, Pipecat, ElevenLabs TTS, Deepgram ASR, Anthropic intent
- Geospatial indexing + media pipelines (Lambda + MediaConvert + CloudFront)
- Smart Recruiter backend: 516 TS files / ~101k LoC NestJS + ~17k LoC Python WebRTC microservice
- Engineered enterprise IoT desktop applications — 20k+ LoC, 1,000+ Jest tests
- Architected real-time MQTT communication and concurrent schedulers — ~3× performance improvement
- Stack: Angular, Electron, RxJS, NestJS, RxDB, Python, MQTT pub/sub
Stack
What I build with
Voice AI and LLMOps day-to-day. Heavy lean on Claude, MCP, and the agentic stack.
Voice AI
AI / LLMs
AI Dev Tools
Backend
Frontend
Cloud / DevOps
Data
Observability
Writing
Latest articles
Available for senior AI / contract / FDE work
Building something with AI?
Voice agents, MCP servers, LLM pipelines, agentic workflows — pick a slot, drop a message, or send your email and I'll reply within a day.
Replies within ~24 hours · Remote-first · global · open to relocation