Vetted OpenAI API Professionals

“Senior full-stack developer who has operated in lean, multi-product engineering environments with significant end-to-end ownership. Strongest in JavaScript/TypeScript, React, and Next.js, with hands-on experience building a custom media player and internal business operations platforms that improved efficiency through dashboards, RBAC, search, and performance optimization.”

Benjamin Evans

Screened

Mid-level XR Software Engineer specializing in mixed reality and interactive applications

Nashville, TN5y exp

“Interactive systems engineer with a niche background building high-visibility Unity experiences for museums and public venues, including the Smithsonian and Georgia Aquarium. Has led architecture for multi-kiosk networked installations, integrated OpenAI-powered character experiences, and rebuilt a social-VR face/eye-tracking app with on-device ML, localization in 30 languages, and Steam delivery.”

“Startup-focused full-stack engineer who has worked across fintech and digital health, including Pivotxy and Cybele Health. They combine backend/API development with AI integration, including GPT-powered financial reporting and a finance agent benchmark, and have helped turn manual report workflows that took weeks into outputs generated in minutes.”

“Backend-focused engineer who built a Python-based automation system leveraging Gemini AI and prompt-driven PDF field extraction to replace a previously manual third-party workflow. Drove stakeholder alignment around accuracy/acceptance thresholds and added production-minded safeguards like graceful failure handling and backup model contingencies.”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

“New grad who shipped an LLM-powered writing app (“Write-it”) to production on Azure with CI/CD (GitHub Actions + JFrog) and implemented an unconventional RAG pipeline to prevent repetitive prompts using embeddings and cosine similarity. Also participated in a Luma AI image/video generation hackathon, iterating with artist feedback and improving usability by rewriting non-technical prompts via an LLM.”

“Music editor/composer and sound designer who treats audio as narrative—e.g., created a dialogue-free short film where music and SFX carried the story across multiple emotional chapters. Works across Ableton, Logic, and Pro Tools with iZotope RX for VO cleanup, and has adjusted mixes for YouTube/podcast retention (increasing music/SFX levels led to longer viewer watch time).”

“Frontend engineer with experience building an orthodontics-focused IoT web product, including a complex React + TypeScript 3D treatment viewer with a layered architecture (data layer plus multiple canvas asset managers). Has applied performance techniques like caching and Web Workers to keep the UI responsive under heavy computation, and shipped monetization features such as payments and coupon/discount flows with structured QA rollout.”

“Backend Python engineer who owned the full backend build of an AI-driven platform for UK golf clubs, including FastAPI microservices, vector search, and a tuned LangChain+Pinecone RAG pipeline focused on cost and hallucination reduction. Experienced deploying Django/FastAPI/Flask stacks on AWS-backed Kubernetes with GitOps/ArgoCD-style delivery, plus executing legacy-to-AWS migrations and building Kafka-based real-time analytics pipelines.”

“Software engineer (internship experience) who built and owned an AWS serverless multi-user “challenge” feature end-to-end (UI + REST APIs + DynamoDB + deployment), delivering measurable gains in latency (-30%), debugging time (-50%), and join drop-offs (~-30%). Also productionized a multilingual RAG-based QA system with vector retrieval and guardrails, improving accuracy to ~85% and driving ~20% DAU growth.”

“Unity mixed-reality developer who shipped ZenPlay, a multiplayer Go app on Meta Quest, integrating a C# rules engine with XR input, Meta avatars, Hathora-hosted matches, and Vivox voice chat (reported ~700 MAU). Also built a production LLM agents backend (LangChain + RAG with Pinecone/ChromaDB + ChatGPT) powering embodied conversational avatars, with a strong focus on streaming voice latency optimization (ElevenLabs TTS) and cross-platform WebXR delivery (Quest/iOS/Android).”

Revanth P

Screened

Mid-level DevOps Engineer specializing in AWS, Azure, Kubernetes, and GenAI infrastructure

Walnut Creek, CA4y exp

“Database/platform engineer with stronger hands-on experience in AWS and Azure than GCP, but able to speak credibly about cloud database architecture, automation, and reliability engineering. They led an on-prem MySQL to RDS/DynamoDB migration, built Terraform/Python-based zero-touch database operations, and described a performance incident where latency dropped from 2s to under 300ms while supporting 2x traffic.”

“Built and shipped Site Audit AI, a production multi-turn Claude-based agent that autonomously crawls websites, calls tools, and generates scored audit reports—reducing a manual 2-3 hour developer workflow to under 60 seconds. Also brings practical experience integrating inconsistent payroll/HR data across platforms like QuickBooks and Keka, with a strong focus on validation, fault tolerance, and resumable workflows.”

“Backend/AI engineer who built an enterprise RAG chatbot over 40,000+ technical documents, owning the system from ingestion and retrieval design through launch, optimization, and incident prevention. Stands out for treating LLM reliability as a data, retrieval, and observability problem—delivering 90%+ benchmark accuracy, ~50% fewer hallucinations, and major gains in lookup speed and latency.”

Narayan Anantha Krishnan

Screened

Junior AI/ML Engineer specializing in LLMs, RAG, and cybersecurity

Syracuse, NY2y exp

“AI/full-stack builder with hands-on experience shipping conversational and agentic products, including a travel itinerary assistant, a multi-agent data analysis platform, and a self-correcting RAG system. Also brings academic research depth from Syracuse University, where they helped develop tiny-LLM-based IoT threat mitigation and presented an accepted paper at FLAIRS 39.”

“Full-stack engineer who architected a university-wide assessment reporting platform integrating rich-text inputs, Oracle data, SSO, and bulk PDF generation. Stands out for pragmatic decision-making in low-dependency environments, strong collaboration with non-technical stakeholders, and hands-on performance work that improved banking page load times by up to 80% at Dapi.”

SIVA RAMAKRISHNA PALADI

Screened

Mid-level AI/Full-Stack Engineer specializing in agentic AI and RAG systems

Santa Clara, CA5y exp

“Solo builder who shipped two ambitious AI products from scratch: Zoly, a healthcare/pharmacy automation platform with voice agents, RAG, clinician dashboard, and patient app live in 4 months, and Breeth, a contextual memory system for AI agents deployed on AWS. Particularly compelling for teams needing a hands-on full-stack/AI engineer who can operate in ambiguity, design for safety and compliance, and turn complex agent workflows into production products.”

“New grad software engineer who has already built both a full-stack location-based social app and an internal AI on-call copilot using OpenAI and LangChain. Stands out for combining end-to-end product execution with practical LLM engineering, including RAG, fallback design, citations, and production evals, plus shipping a hackathon-winning MVP in 24 hours.”

“Software engineering intern who built and deployed a full-stack telemedicine platform (React/Node/MongoDB) used daily in a pediatric clinic, incorporating PyTorch-based predictive features. Demonstrated strong customer-facing iteration and production performance debugging—resolved a live slowdown by indexing/optimizing MongoDB queries and adding caching, improving response times by ~50%.”

“Student technologist building production-oriented AI products, including a college guidance chatbot for Track2College and a voice-based travel assistant. Strong hands-on experience with RAG systems, FastAPI backends, TypeScript frontends, retrieval evaluation, and tool-using LLM workflows, with a clear focus on grounded, reliable user experiences.”

“Built AI-powered products across both healthcare and financial services, including a privacy-conscious assistant for elderly health check-ins and a production RAG system for high-stakes financial document analysis. Stands out for combining full-stack engineering with strong LLM reliability practices—grounding, structured outputs, fallback handling, monitoring, and human-in-the-loop controls—while delivering measurable impact on accuracy, speed, and system performance.”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Built and shipped production LLM agents for real-time, high-concurrency conversational systems, including a RAG-based pipeline with dynamic multi-provider routing and failover that achieved 99.99% reliability and sub-800ms latency. Also architected a UAV telemetry chatbot with tool-calling (anomaly detection/summarization), strict schema validation, and robust eval/monitoring loops, cutting tool-call errors by 30% and reducing operational costs by 90%.”

Rizwana Shaik

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native apps and AI copilots

Dallas, TX4y exp

“Internship project building and deploying a LLaMA-based, RAG-enabled copilot inside a Professional Services Automation platform, enabling natural-language navigation, text-to-SQL reporting, and project/resource/budget insights across multiple modules. Addressed real production issues like context drift and vague queries with hybrid search, metadata enrichment, and an intent classification/rewriting layer, orchestrated via Apache Airflow—ultimately cutting PMO reporting time by 40%.”