Vetted Large Language Models (LLMs) Professionals

“AI/backend-focused builder who has shipped two distinct applied AI products: a game discovery platform with vector search + RAG chat, and an AI accounting platform for small businesses. Stands out for combining product discovery with hands-on system design, including sub-100ms retrieval performance, privacy-conscious financial workflows, and measurable impact like 58% compute-time reduction and support for 24,000+ user profiles.”

“Full-stack engineer with hands-on ownership of production systems, including a Kafka-based notification/alerting platform (Node.js + React) deployed on AWS with Docker/GitHub Actions, achieving ~95% email delivery reliability. Demonstrates strong operational maturity (observability, CI/CD, zero-downtime migrations) and experience shipping in ambiguous environments (SJSU project) with evolving requirements.”

“Backend/AI engineer with production experience in legal tech: built a high-scale licensing/subscription API (FastAPI/Postgres/Stripe) and shipped a RAG-based chatbot for an eDiscovery platform. Designed a robust legal document ingestion workflow that processes thousands of documents into a searchable vector index with clear retry/escalation logic, and has demonstrated measurable Postgres performance wins (200ms to 10ms) using EXPLAIN ANALYZE and composite indexing.”

Lando Ringel

Screened

Senior Unity Developer specializing in AR/VR, AI, and game systems

Melbourne, FL11y exp

“Game developer with a distinctive niche in integrating LLM-driven dialogue directly into Unity gameplay, including a full web-hosted architecture using llama.cpp, FastAPI, and cloud GPUs for real-time NPC interaction. Also brings VR/mobile optimization experience from a drone simulation project at GovScio, combining AI systems thinking with practical 3D environment and performance work.”

“Amazon account management professional with strong marketplace GTM instincts who draws direct parallels between e-commerce growth and B2B SaaS go-to-market work. Most notably, they say they scaled a startup Amazon Turkiye marketplace into a US-competitive operation, generating $100K+ with 25% ROI and 83x ROAS on just $1,200 in ad spend, while emphasizing long-term organic growth through SEO and listing optimization.”

“Built and deployed a production RAG-based internal document Q&A system using LangChain, vector search, and a dockerized FastAPI LLM service. Focused on reliability by systematically reducing hallucinations and improving retrieval through prompt grounding/abstention strategies, chunking and top-k tuning, and iterative evaluation with logged metrics and manual validation.”

Venkata Bayarapuneni

Screened

Junior Full-Stack Developer specializing in JavaScript, Python, and cloud-deployed web apps

Remote, USA1y exp

“Built and deployed a production LLM-powered travel assistant (Globe Trote) that automates end-to-end trip planning using a multi-step agent pipeline with RAG, external API calls, and enforced structured JSON outputs. Focused on reliability (validation, retries, fallback prompts, logging) and reported a ~30–40% reduction in irrelevant/generic responses after adding retrieval grounding.”

SaiDheerajReddy Gadikota

Screened

Mid-level AI Engineer specializing in agentic systems and enterprise LLM platforms

USA, USA4y exp

“Current AI engineer at a startup who has spent the last year architecting multi-agent systems for software development workflows. Stands out for combining LLM speed with engineering discipline—using tools like Pydantic, LangGraph, and LangChain to build reliable, production-ready agent workflows with validation, routing, and retry logic.”

“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”

“Full-stack engineer at Srasys Inc. who built and owned production payments/checkout for an e-learning platform serving 5,000+ users using Next.js App Router + TypeScript. Deep focus on correctness and reliability (Stripe webhooks, signature validation, DB-level idempotency) plus measurable performance wins (~40% latency reductions) through Postgres indexing/EXPLAIN ANALYZE and Redis-backed caching with CloudWatch monitoring.”

“LLM/RAG engineer at Connex AI who built and deployed a production healthcare agent to extract clinical insights from medical data/notes. Strong focus on real-world reliability—hallucination mitigation (citations, schema validation, confidence thresholds, rejection logic), custom LangChain orchestration (query rewriting, fallback paths), and production evaluation/observability—while collaborating closely with clinical SMEs to ensure clinical fit and time savings.”

“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”

Lalith Sagar Kambala

Screened

Junior Full-Stack Software Engineer specializing in AI-powered SaaS

United States, USA1y exp

“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”

“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”

“Early-stage product engineer who built an AI persona chat system end to end at Super Intro, spanning Next.js frontend, GraphQL/real-time backend, retrieval memory, and LLM-based matching. They combine strong TypeScript rigor with practical AI systems design, and cite measurable impact including ~40% engagement growth, ~30% recall improvement, and lower LLM costs in production.”

“Full-stack product engineer who has built both a production-grade React/Next.js analytics platform and a Stripe-based subscription system end to end, with measurable business impact including churn reduction, reporting automation, and cost savings. Also stands out for 0-to-1 work in the digital art space, where they built an AI-powered cataloguing and delivery pipeline over 30TB of assets using computer vision, Shopify, and cloud infrastructure.”