Vetted Large Language Models (LLMs) Professionals

“Seed-stage startup engineer owning features end-to-end across full-stack development, DevOps, rollout, and post-launch maintenance. Built data ingestion and evaluation workflows for an LLM data-quality platform using Next.js, MongoDB, Postgres, and GCP Pub/Sub, with a strong focus on reliability, caching, and pragmatic performance improvements.”

“Built GenMedX, a multi-module clinical AI system for emergency department decision support spanning triage prediction, diagnosis, medication Q&A, and visit summarization. Stands out for combining medical LLM fine-tuning, RAG, and rigorous evaluation/monitoring to drive a major triage recall improvement from 38.5% to 76.6%, with a strong focus on safety, edge-case detection, and production reliability.”

“Technical lead who shipped a production GPT-4-powered customer support agent for Square, serving a large fintech customer base through a React chat interface with tool-using orchestration, guardrails, and live handoff paths. Brings strong real-world experience in agent reliability, evaluation, observability, and workflow orchestration using Temporal, Sidekiq, Pinecone, Datadog, and Snowflake.”

“AI product leader with end-to-end experience building revenue-generating products and internal AI platforms across legal tech, edtech, and neurofeedback healthcare. At Clio, they helped launch the company's first AI offerings, drove $60K MRR in two months, scaled the AI team from 15 to 45, and enabled 12 additional AI applications across the business. They also bring a strong human-centered AI perspective from building tutor matching, lesson-planning, and clinician recommendation systems.”

Doran Morgan

Screened

Senior Product Management leader specializing in AI-enabled enterprise SaaS platforms

Atlanta, GA16y exp

“Product leader with experience across Sage Intacct, Salesforce, Deltek, and Warner Brothers-style enterprise environments, spanning payments, legal workflow automation, compliance, and global team leadership. Notably led a new customer payment portal that lifted payment completion from 20% to 38% and built AI-assisted contract workflow improvements that reduced false positives and redirected 2 Legal Ops FTE to higher-value work.”

Anushka Bhagchandani

Screened

Intern Product & Software Engineer specializing in GenAI/LLM and e-commerce platforms

San Francisco, CA2y exp

“Software engineer (2+ years in India) and current GenAI intern who shipped LLM-powered review-writing enhancements at Myntra (Walmart-backed), using pilots and A/B tests to lift review quality by 5% in 30 days. Demonstrates strong LLM operations discipline (logging, dashboards, alerts, rollback) and fast incident response, plus experience delivering developer-focused workshops and public technical talks.”

“Aspiring founder exploring a deep-tech startup at the intersection of HPC and AI, focused on tightly integrated systems that generate high-quality simulation data for applications like drug discovery and weather prediction. They appear especially motivated by unmet market opportunities and take a pragmatic view of startup risk, prioritizing ideas with strong growth potential that can become real within five years.”

“Candidate describes a development approach centered on AI-assisted coding, testing, and agent-driven workflows, including production exposure to multi-agent systems and governance-oriented logging. They appear particularly focused on combining AI speed with structured validation through unit tests, boundary tests, and edge-case monitoring.”

“Senior frontend engineer focused on complex internal operations and payment products, with deep experience building React/TypeScript dashboards for payments, subscriptions, and observability workflows. Stands out for going beyond UI implementation to shape API contracts, real-time architectures, performance strategy, and product behavior across support, finance, and engineering use cases.”

“AI/LLM-focused engineer with hands-on experience building RAG pipelines, prompt engineering workflows, and multi-agent systems using tools like LangChain. Stands out for combining AI-assisted development with production-grade validation and for leading the architecture/orchestration of agent-based recommendation systems that improved response time, accuracy, and scalability.”

“Engineering leader with hands-on architecture depth who has managed offshore teams up to ~30 engineers across mobile, web, backend, and Roku. Particularly strong in applying AI pragmatically—driving code-generation and review workflows, tailoring model usage by tech stack, and building internal LLM/RAG tools—while also improving operational KPIs through automation, cost optimization, and workflow redesign in content/media environments.”

Swarnabha Roy

Screened

Mid-level Robotics & Computer Vision Engineer specializing in autonomous systems and edge AI

College Station, TX6y exp

“Robotics/perception researcher (MVOS Lab, South Dakota State University) who built an end-to-end multimodal RGB-D + LiDAR pipeline for autonomous greenhouse harvesting and 3D plant phenotyping. Demonstrated strong production ownership by diagnosing motion blur with ROS-bag + OpenCV metrics and shipping an edge-deployed, scan-quality-aware workflow that boosted barcode read rate to 98% and supported ~70% autonomous pepper detection/harvesting accuracy.”

Yash Jajoo

Screened

Senior Software Engineer specializing in AI and FinTech platforms

New York City, NY8y exp

“Built a production LLM pipeline at Walter AI that scans massive user inboxes, identifies financial newsletters, and extracts trading strategies into structured JSON for downstream paper-trading workflows. Stands out for combining agent architecture with strong production discipline—cutting scan time from 20 to 5 minutes, reducing LLM costs by 90%, and achieving 3-second P99 latency while handling messy, inconsistent email data at scale.”

Akhilesh Patil

Screened

Junior AI/ML Engineer specializing in FinTech and generative AI

Remote, USA2y exp

“Built an end-to-end AI bug triage dashboard that combined React/TypeScript, FastAPI, Postgres, and classical ML to reduce manual engineering triage work by about 40%. Stands out for pragmatic, product-minded AI engineering: choosing interpretable models when they were sufficient, designing human-in-the-loop UX for trust, and separately building an agentic RAG project with vector search, Neo4j knowledge graphs, and reranking.”

Kevin West

Screened

Executive engineering leader specializing in AI, cloud architecture, and startup product delivery

Phoenix, AZ15y exp

“Founding/backend engineering leader who built an AI product for car enthusiasts, recruited and managed cross-functional/offshore teams, and helped drive an $8M Series A after scaling a viral launch of 40k+ users in a weekend. Also built a sophisticated human-in-the-loop AI brand platform with agent orchestration and led a major AWS Step Functions migration that reduced platform costs from $30k/month to roughly $900/month.”

Josh Rhoades

Screened

Executive Technology Leader (CTO) specializing in AI-enabled SaaS and regulated platforms

Los Angeles, CA30y exp

“Senior engineering leader with experience at Disney and BlackLine who drives business-aligned technology roadmaps through deep Product/Engineering partnership (two-in-a-box) and pragmatic prioritization frameworks. Has led major modernization initiatives—private-to-public cloud migration to GCP with multi-cloud evolution, data-layer performance improvements (Mongo/Redis, caching/query optimization), and tooling upgrades (VSS to GitHub)—while scaling teams with strong quality and accountability culture.”

Kevin Hoffman

Screened

Director-level product and engineering leader specializing in distributed systems and cloud platforms

Windsor, CT16y exp

“Former CTO and co-founder of Cosmonic who led technology vision, roadmap, and team building while also serving as the technical lead in investor and VC meetings. Has hands-on experience supporting seed and Series A rounds, plus strategic exposure to later-stage fundraising at Synadia, combining startup-building depth with a pragmatic view of risk and business viability.”