Vetted Founding Engineers

“Founding engineer at a civic-tech startup (Barrow) who built and operated a Next.js/TypeScript product with map-based public reporting, including clustering and dynamic geospatial loading to improve UX and performance. Also implemented a location-aware RAG chatbot using Pinecone, web scraping/transcription, caching, and fallback web search, and owned post-launch observability plus scaling decisions (load balancing/horizontal scaling) based on API usage patterns.”

“Automotive software and OTA/infotainment platform leader who has repeatedly built new lines of business as an intrapreneur—most recently taking an infotainment app marketplace from concept to production in <7 months with $3M seed funding and delivering ~$200M ROI while scaling the team from 0 to 90. Deep hands-on experience solving OTA fragmentation across ECUs/telematics and multiple OS/backends, with 18 patent processes submitted; exploring an AI-driven platform to automate OTA software qualification and cut release cycles from 9–18 months to ~2 weeks.”

“Founding engineer for an AI product (“world’s first funny AI”) who designed and implemented the full-stack architecture (React/TypeScript + Node) and migrated production from Vercel to AWS. Shipped a Lambda-based image pipeline that eliminated lag/missing images and brought page load times to under a second, and has hands-on experience integrating multiple LLM providers (OpenAI, Claude, Gemini, Grok) with structured-output and self-check reliability techniques.”

“Built a production Python microservice around Grafana Loki focused on reliability, with checkpointing, idempotency, replay tooling, tracing, and alerting to prevent data loss and silent lag. Also has hands-on experience hardening brittle Playwright automations against dynamic UIs, auth expiry, rate limits, MFA, and bot-detection constraints, plus turning tribal-knowledge SOPs into explicit state-machine-driven workflows.”

“AI-focused product engineer working on LLM routing, prompt engineering, and multimodal API integrations at production scale. They describe improving system accuracy, latency, and token usage, fine-tuning an internal model to reduce third-party API dependence, and adding safety guardrails through prompt-injection testing and red-team evaluation.”

“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”

“Engineering Manager/player-coach leading Data Infrastructure, ML/DS, and AI Engineering pods who recently shipped multiple production agentic GenAI features. Built privacy-preserving LLM workflows (PII redaction via Microsoft Presidio) and drove an AI expense-approval agent from ambiguous ask to GA, cutting approval time from ~2.5 days to <4 hours with >85% accuracy. Also owned a major LLM cost overrun incident and implemented cost observability plus circuit breakers to prevent runaway agent loops.”

“ML practitioner with hands-on experience in healthcare time-series modeling (CGM-based blood glucose prediction) including a novel ICA-based blind source separation approach and robust data-cleaning for noisy, missing sensor data. Also built an embeddings + LLM-powered podcast recommendation workflow using YouTube transcript scraping and Vellum AI document indexing, with a strong emphasis on production-grade engineering practices (TDD, monitoring) and realistic rolling validation for forecasting.”