Vetted OpenAI API Professionals

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

“Automation-focused engineer with hands-on experience building production Python integrations, maintaining 60+ Jenkins pipelines across six product lines, and hardening CI systems in real-world environments. Most notably, they turned a tribal-knowledge Windows server maintenance process into a production workflow across nine servers, saving about 500 engineering hours annually.”

Devang Borkar

Screened

Mid-level AI Engineer specializing in LLM agents and evaluation systems

California, USA4y exp

“Built an end-to-end Python integration for an emotion-aware presentation feedback system that processed uploaded or live video and analyzed facial emotion, tone, and gestures. Also has Playwright automation experience in a loan management workflow, with emphasis on reliability, observability, security, and iterative delivery under ambiguous requirements.”

“Full-stack engineer with end-to-end ownership of a production customer plan activation and account management flow at T-Mobile, spanning Java/Spring Boot APIs, React frontend, and Docker-based CI/CD deployments. Demonstrated performance/scalability work (query optimization, indexing, caching) and measured success via improved retrieval speed and reduced support tickets.”

Aswin Jojo

Screened

Junior Software Engineer specializing in full-stack development and machine learning

Houston, TX3y exp

“Full-stack engineer with experience owning products end-to-end in both insurtech/financial workflows and AI-enabled IT operations. Built scalable React/Node and FastAPI systems, improved reliability under peak transaction load with SQS/Redis, and shipped an AI ticket-classification platform that cut response times from 3 days to 1 day.”

rhema junapudi

Screened

Mid-level Full-Stack Developer specializing in cloud-native healthcare and insurance platforms

Albany, OR3y exp

“Full-stack engineer with healthcare and claims-domain experience who has shipped AI-assisted documentation and fraud-scoring systems using Angular, ASP.NET Core, OpenAI APIs, and ML tooling. Stands out for building compliance-aware, traceable agentic architectures with graceful degradation, and for a nuanced understanding of AI failure modes in production.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

“Early-career engineer who built and launched a zero-to-one AI-driven approval workflow at SDSU that is used daily by roughly 2,000 university users. They owned the system end-to-end—from FastAPI/PostgreSQL backend to React UI—and showed strong judgment around LLM reliability, using a two-step pipeline, validation checks, and human-review fallbacks to cut manual processing time by about 80%.”

Bhagyashree Patil

Screened

Intern Applied AI Engineer specializing in LLM systems and data engineering

Los Angeles, CA1y exp

“Full-stack engineer with hands-on production experience across both traditional SaaS and LLM-powered support tooling. They owned a real-time ecommerce order tracking dashboard that improved support response times by 40%, and helped ship an AI support assistant using the OpenAI GPT API that cut ticket handling time by 30% through strong prompt design, retrieval grounding, validation, and human-in-the-loop safeguards.”

Raj Kalantri

Screened

Intern-level Software Engineer specializing in AI/ML and full-stack development

Kolkata, India1y exp

“Built a sophisticated AI career counselor as a full-stack web app for early-career students, integrating React, Flask, Pinecone, and LLM inference into a stateful conversational product. Stands out for combining hands-on debugging of retrieval/embedding pipelines with strong browser-performance instincts and pragmatic UX iteration based on real user testing.”

Sri Teja Adimulam

Screened

Mid-level AI Engineer specializing in LLM systems and cloud data platforms

Detroit, MI5y exp

“Customer-facing technical professional with 5+ years supporting AI, analytics, and automation solutions in enterprise environments. Currently helping build and deploy a multi-agent AI system for roadside assistance operations, with strong experience translating complex business workflows into demos, pilots, integrations, and secure production-ready architectures.”

“GenAI engineer who has deployed production LLM/RAG chatbots for internal document search, focusing on reliability (hallucination reduction via prompt guardrails + retrieval filtering) and performance (latency improvements via caching). Experienced with LangChain/LangGraph orchestration for multi-step agent workflows and iterates using monitoring/logs and benchmark-driven evaluation while partnering closely with product and business teams.”

“AI/LLM practitioner at iFrog Marketing Solutions who drove a RAG chatbot from prototype to production in a legacy, AI-resistant environment by validating customer needs and building a business case. Implemented production-grade LLM practices (CI/CD eval gating, rollbacks, prompt/context engineering) and led internal workshops to bring non-AI-native developers up to speed while partnering with sales on tailored demos to drive adoption.”

“Backend engineer with hands-on ownership of FastAPI/Django services using MongoDB and React integration, focused on production reliability and performance (Redis caching, Celery background jobs, automated testing). Has delivered AWS container deployments via GitHub Actions to ECR with scripted rollouts/health checks, and supported phased migrations with replication and rollback planning. Also built a real-time user-activity streaming pipeline addressing partition hot spots and consumer lag through partition-key strategy, idempotency, and monitoring.”

“Software engineer with experience delivering customer-facing and internal tools across GE Renewables, GE Healthcare (supply chain/production systems), and a Boulder-based event app startup. Recently focused on scaling backend performance using Redis and RabbitMQ, and has hands-on experience resolving hard-to-reproduce production issues in legacy authentication/session systems; also deployed a personal project (Journal Buddy) publicly.”

“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”

“Backend-leaning full-stack engineer who ships AI-enabled products end-to-end: built CodeChat, a production internal codebase Q&A tool using RAG with Pinecone and a model-agnostic wrapper across OpenAI/Anthropic/AWS Bedrock, cutting AWS costs ~50% and latency ~45%. Also built and operated RealityStream, a Flask-based real-time forecasting API with JWT/RBAC, MLflow model versioning, and Prometheus/Grafana observability, including handling a real production latency incident via rollback, preloading, and caching.”

“Early engineer at a fast-growing startup who owned an AI-powered portfolio/site generation workflow end-to-end (frontend in Next.js App Router/TypeScript through backend orchestration). Emphasizes server-first security/performance (Server Components/Actions, revalidation), and production hardening with validation, caching, observability, retries/idempotency, and CI/E2E testing.”

“Junior Unreal Engine developer who owned end-to-end UI implementation for a UE5 horror-game vertical slice, building production-minded subtitle and interaction UI with responsive UMG layouts and polished transitions. Demonstrates strong UI architecture instincts (MVVM-style separation, layered screen ownership, cross-input focus behavior) and uses Unreal profiling tools to diagnose and reduce redundant UMG updates.”

“Backend/full-stack engineer from Foxconn Industrial Internet who led development of a production TypeScript/Node.js facility monitoring platform delivering near real-time manufacturing metrics (e.g., downtime and OEE) using MySQL + InfluxDB and a React dashboard. Demonstrates strong production operations mindset with queue-based workers, idempotency/DLQ patterns, structured observability, and automated Docker + GitLab CI/CD deployments.”

Anna Niedzialek

Screened

Intern Software Engineer specializing in AI/ML and computer vision

Warsaw, Poland0y exp

“Backend-focused Python engineer who owned and deployed EcoHero, a recycling guidance app using FastAPI + Firebase with barcode lookup, ZIP-code-based state rules, and user history tracking backed by 50 state datasets. Has hands-on Kubernetes + Docker experience and uses GitHub Actions and GitOps-style PR workflows for consistent deployments, plus event-driven async processing patterns with idempotency and retries.”

Dwarakesh Rajesh

Screened

Intern Robotics/Mechatronics Engineer specializing in automation and ROS2 systems

Boston, MA1y exp

“Robotics software builder who developed a solo fault-adaptive robotic arm: current-based joint health monitoring feeding an ESP32, ROS 2/MoveIt 2 motion planning that adapts to joint failures, and a custom brute-force IK solver to overcome URDF/MoveIt singularity issues. Also worked on real-time 12-microphone sensor-fusion audio processing for drone navigation, resolving buffer/noise problems with multithreading and chunked sampling; experienced with Webots/CoppeliaSim and learning Isaac Sim.”

Zubair Shaik

Screened

Mid-level Full-Stack Developer specializing in AI-driven FinTech platforms

Remote, USA4y exp

“Built and productionized an LLM-powered loan decisioning agent at Bank of America, integrating RAG with microservices to automate creditworthiness assessment and recommendations. Emphasizes real-world reliability and governance (EKS autoscaling, observability, SOC2/PCI security controls), and drove measurable outcomes including 20% faster loan decisions and a reduction in agent failures/fallbacks to under 2% through schema enforcement and confidence-based routing.”

Adhiraj Chadha

Screened

Junior Software Engineer specializing in AI, data, and full-stack applications

Amherst, MA3y exp

“Builder with a mix of backend engineering, product instinct, and startup execution: they shipped a legal BI platform from scratch that handled 1,000+ cases, cut reporting time 80%, and saved $30K annually. They also move quickly in ambiguous environments, from launching a roommate app across iOS/Android after user discovery to building a RAG system with a 50+ case evaluation suite and a cloud dev environment in under 48 hours.”