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Vetted GPT-4 Professionals
Pre-screened and vetted.
Junior analytics consultant specializing in FinTech and financial modeling
Junior Software Developer specializing in backend and AI-powered web applications
Shreyas Krishnareddy
Junior AI/Software Engineer specializing in NLP, RAG, and resume parsing
“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”
HarshelSrivatsava Srivatsava
Intern Full-Stack Engineer specializing in AI-powered SaaS products
“Solo builder of OGym, shipping production AI features for gyms that turn member behavior/health data (workouts, attendance, nutrition, payments, device metrics) into prioritized, actionable owner and member insights. Designed and implemented FastAPI backends, PostgreSQL-based RAG workflows, guardrails (RBAC/validation/rate limiting), and real-user evaluation loops, with a strong focus on latency/cost optimization and reliable data pipelines.”
Christopher Paredes
Junior Full-Stack Developer specializing in web development and AI research
“Early-career software engineer who has already owned a real-world web product end-to-end for a 100+ member university ACM organization, including requirements gathering, UI/UX improvements, hosting, CI/CD, and ongoing maintenance. Brings hands-on React and full-stack project experience with a strong bias toward practical execution, mobile-friendly design, and reliable delivery.”
Gowtham Manikanta Chilukabathula
Entry-level AI and Data Analyst specializing in LLMs and analytics
“Candidate brings a blend of AI, analytics, and go-to-market support experience through an AI/data internship and graduate assistant role. They analyzed data across 50+ organizations to identify high-fit outreach segments, improving targeting efficiency by about 28%, and also built/reviewed GPT-4 and LangChain-based outbound messaging systems with strong quality controls.”
Entry-level Data Scientist and Software Engineer specializing in AI and data pipelines
Mid-level ML & Full-Stack Engineer specializing in LLM systems and RAG
Entry-level Software Engineer specializing in full-stack, backend, and cloud development
Junior Frontend Software Engineer specializing in React, Next.js, and TypeScript
Junior Backend Software Engineer specializing in Java/Spring Boot and AWS
Mid-level AI/ML Engineer specializing in LLM-powered RAG systems and MLOps
Senior Full-Stack & AI Engineer specializing in SaaS and LLM applications
Mid-Level Full-Stack Software Engineer specializing in cloud-native systems
Mid-Level Full-Stack Software Engineer specializing in healthcare web apps and LLM integrations
Mid-level Software Engineer specializing in Generative AI and cloud-native microservices
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”
Jeevan Kumar
Mid-level Full-Stack AI Engineer specializing in LLM systems and RAG
“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”