Vetted OpenAI API Professionals

Vengalarao Pachava

Screened

Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems

Irving, TX2y exp

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

Monish Reddy Bonthu

Screened

Mid-Level Full-Stack Engineer specializing in real-time systems and FinTech

FL, USA5y exp

“Backend engineer with hands-on experience modernizing a real-time logistics/tracking platform from a tightly coupled polling architecture to a service-oriented/microservices design using Node.js and WebSockets. Emphasizes contract-first FastAPI development, defense-in-depth security (JWT/OAuth, RLS/Supabase), and safe incremental migrations with feature flags and strong observability, delivering sub-second updates and improved performance under peak load.”

“Unity/C# tech lead specializing in multiplayer systems and networking reliability: built a transaction/prediction-based multiplayer inventory system using snapshots/deltas that eliminated duping and supports very large item transfers via RPC. Also implemented an LLM-driven localization pipeline (SGLang on DGX Spark with Gemma Translate) with automatic OpenAI fallback and human QA, and has shipped VR work on Meta Quest 3 with cross-platform input abstraction (mobile/VR/web).”

“Independent builder of production-grade systems: shipped an end-to-end URL shortener with JWT auth, Redis rate limiting/caching, Postgres, Docker, and real-time analytics, and separately architected a Redis-backed distributed task queue handling 1000+ tasks/min. Demonstrates strong distributed-systems instincts (atomicity, retries/DLQ, idempotency, heartbeats) plus a focus on maintainable code and self-documenting APIs (FastAPI/OpenAPI, versioned routes).”

“Founder of distribb.io, an 8-month-old AI SEO software company already at $10k MRR. Brings 15 years of startup ecosystem exposure and a pragmatic, market-driven approach to building businesses by targeting proven demand rather than chasing novelty.”

“Built a production ad-spend optimization system that combined deterministic audit logic with LLM-generated explanations, surfacing severe inefficiencies including 70-90% wasted spend in some Google Ads accounts. Stands out for pairing measurable business impact with pragmatic AI safety and usability decisions, including approval-gated execution and structured, human-readable recommendations.”

“Production-focused ML/AI engineer with hands-on ownership across classical ML and GenAI systems, from CV/NLP services to enterprise RAG. Stands out for combining research-to-production execution with measurable business impact: 40% processing-efficiency gains, 35% fewer support tickets, 5x latency improvement, and 3x throughput gains while maintaining safety and quality.”

“Backend/AI engineer who built Matchable, an end-to-end AI-powered workforce matching platform using FastAPI, transformer-based NLP, PostgreSQL, and AWS, with a strong focus on practical system design tradeoffs. Also brings research-oriented experience from Los Alamos/ASU simulation work and has built multi-agent LLM workflows with schema validation and auditability, suggesting a thoughtful approach to reliability in AI systems.”

“Frontend-leaning full-stack engineer who has rebuilt a high-volume order management system in Next.js/TypeScript for 6000+ active orders and also owned end-to-end product/data architecture in Firebase/Firestore. Stands out for strong performance instincts, type-safe frontend architecture, and pragmatic 0→1 execution across UI, APIs, and data pipelines.”

Venkatasai Vorsu

Screened

Mid-level Full-Stack Software Engineer specializing in Java microservices and cloud platforms

Houston, TX5y exp

“Full-stack engineer with strong React/TypeScript and Spring Boot experience in banking and financial systems, focused on real-time transaction monitoring and payment tracking products. Stands out for scaling high-volume dashboards, solving rendering bottlenecks in live data UIs, and owning features end-to-end from frontend through APIs, Oracle data layer, cloud deployment, and production monitoring.”

Parth Shroff

Screened

Entry-level Full-Stack Software Developer specializing in React, backend systems, and AI apps

Remote, USA1y exp

“Full-stack engineer who has independently owned and shipped educational web products from concept through launch, including an AI-powered thermodynamics tutoring app and a physics learning platform tied to research publication. Strong in React plus AWS serverless architecture, with a clear pattern of working directly with non-technical stakeholders, iterating from user feedback, and maintaining production systems end-to-end.”

Yatin Rajkumar Kalra

Screened

Senior Full-Stack Engineer specializing in cloud-native AI and Healthcare IT

Remote6y exp

“Built AIO, a 7-agent system that automatically fixes failed GitLab CI/CD pipelines in under 60 seconds using Redis queues, typed TypeScript contracts, and strong observability. Heavy AI user who still applies rigorous human review for logic, security, scalability, and code quality, and has made deliberate architectural choices rather than relying blindly on frameworks.”

“Unity game developer who implemented a blockchain/NFT feature in a mid-core football game, including wallet connection via an external web portal and backend coordination for trading player skins/outfits. Has cloud-focused networking experience (AWS/Firebase) primarily for turn-based multiplayer systems and uses Copilot/VS Code to accelerate boilerplate and architecture exploration.”

“Full-stack engineer with fintech payments experience who shipped an end-to-end guest invoice payment flow emphasizing reliability under retries/failures (idempotency via DynamoDB, async processing with Lambda/EventBridge/SQS + DLQ). Also built a FastAPI backend with Cognito/JWT + scoped guest tokens and a polished React/TypeScript checkout UX, and has performance-focused Postgres/Redis design experience for flash-sale e-commerce workloads.”

“Built and shipped a production multi-agent LLM system at Solena that automated internal project intake, validation, reporting, and stakeholder communications using Python, SQL, and LangChain, with strong emphasis on reliability (structured validation, safe defaults, logging, and state tracking). Also used LangGraph to orchestrate a multi-step video summarization pipeline, and has experience partnering with non-technical stakeholders to define “completion” criteria and reporting needs.”

Manas Agarwal

Screened

Junior Full-Stack Software Engineer specializing in Python APIs, React, and cloud AI integrations

Superior, CO2y exp

“Customer-facing software engineer who builds and deploys practical AI/RAG solutions (e.g., an AI assistant for searching billing PDFs) by deeply understanding support workflows and iterating with users. Demonstrates strong production instincts—quickly stabilizing peak-traffic API timeouts with caching/background jobs, then implementing durable fixes with proper monitoring and maintainable code practices.”

Benjamin Agyekum

Screened

Senior Full-Stack Software Engineer specializing in AI-powered web and mobile applications

California, USA8y exp

“Backend/full-stack TypeScript engineer who has owned end-to-end, production-oriented systems including an AI property management platform (NestJS/Postgres/WebSockets on Google Cloud using Gemini Vision) and an AI logistics platform (Node/Redis queues/Postgres) focused on low-latency, correctness, and observability. Also designed a public GraphQL API and TypeScript SDK for education partners at StudyFetch, citing 40+ partner integrations in the first quarter.”

“FinTech engineer who has owned customer-facing onboarding deployments end-to-end, combining React/Node.js application development with workflow automation and post-launch operational metrics. They also implemented an LLM-driven onboarding assistant using contextual prompts and backend orchestration, and showed practical production experience debugging non-deterministic AI behavior caused by stale pipeline context.”

“Software engineer with a pragmatic, production-oriented approach to AI-driven development, using AI to accelerate coding while keeping human oversight on correctness, architecture, and final decisions. Has hands-on experience with agent-style AI workflows and has led the design and coordination of AI-agent systems with a strong emphasis on reliability, performance, and end-to-end execution.”

“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

“LLM engineer who has deployed a production RAG system (LangChain/FAISS/FastAPI) for enterprise semantic search, tackling real-world latency by LoRA/PEFT fine-tuning and grounding outputs with retrieval. Brings strong MLOps (Docker, AWS EKS, CI/CD, MLflow) plus stakeholder-facing explainability experience using SHAP to align ML-driven financial guidance with non-technical domain experts.”

“Software engineer who built an AI marketing/outreach agent end-to-end: Next.js (App Router + TypeScript) frontend integrated with a Python/Django REST backend using LLMs (Gemini, ChatGPT-4o) and SQL databases. Demonstrated measurable performance wins—improved a 100k-record UI by 15% (Lighthouse) and cut a Postgres-backed search API from ~3s to ~1ms via indexing—while also owning post-launch monitoring (webhooks/cron, New Relic/CloudWatch) and customer support.”