Vetted Retrieval-Augmented Generation (RAG) Professionals

Vatsal Gabani

Screened ReferencesStrong rec.

Mid Software Engineer specializing in backend systems and AI-enabled platforms

California, USA4y exp

“Full-stack engineer with hands-on ownership of a support ticket intelligence platform, spanning React/TypeScript frontend work and backend API, PostgreSQL, Redis, and Docker-based deployment. They stand out for driving practical architecture and performance improvements in production, including moving heavy processing async and cutting response times from about 300ms to 150ms while improving reliability.”

“Senior engineering leader with experience across cybersecurity, retail commerce, consulting, and media platforms, combining large-scale org leadership with hands-on architecture depth. Notable for driving measurable cloud modernization outcomes—multi-million-dollar cost savings, major latency and MTTR reductions, and compliance-heavy transformations—while also leading AI/NLP and consumer product simplification initiatives.”

Vigynesh Bhatt

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in backend, cloud, and ML systems

Salt Lake City, UT4y exp

“Software engineer with experience across Goldman Sachs, BYU Broadcasting, Juniper Networks, and an edtech startup (Doubtnut), spanning data migrations, AWS-based media backends, and microservices observability. Built a Redis/ElastiCache caching layer in front of DynamoDB/S3 to improve media delivery latency and cost, and created an SEO indexing automation tool using the Google Search Console API that saved ~15–30 person-hours per day.”

Venu Dave

Screened ReferencesStrong rec.

Junior Software Development Engineer specializing in backend data platforms and LLM applications

New York, NY3y exp

“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”

Andrew Fares

Screened ReferencesStrong rec.

Mid-level Data Engineer specializing in AI, GenAI, and cloud data platforms

Seattle, WA4y exp

“Built production AI systems inside AWS finance/procurement, including an LLM-based supplier quote classification and price-vetting workflow that drove $5M in savings over 3 months. Combines GenAI evaluation expertise, internal platform design, and reusable Python data-quality tooling with strong cross-functional execution across finance, accounting, and hardware engineering.”

Emily Wood

Screened ReferencesStrong rec.

Director-level Product Management leader specializing in digital media, edtech, and e-commerce

Boston, MA15y exp

“Product leader with experience as the first product manager at Harvard Business Review, where they grew into Director of Product and built out the PM team. More recently, they shipped an AI-enabled learning pathway curation tool using RAG and have been driving platform centralization and human-centered AI strategy in enterprise learning.”

David Proctor

Screened ReferencesStrong rec.

Executive Enterprise Architecture & AI Strategy Leader specializing in modernization and agentic AI platforms

Colorado Springs, CO25y exp

“Technically and operationally oriented builder with startup ideas of their own, including an AI services firm in the ideation stage. Stands out for a practical understanding of venture studios and accelerators, with fluency in founder-market fit, MVP validation, hiring, and investor readiness, and a measured approach to going all-in only on high-signal opportunities.”

“Engineering leader (12+ years Director, 17 years lead) focused on developer productivity and platform/framework work across Oracle, PlayStation, Workday, and CafePress. Notable for building distributed teams from scratch and delivering high-impact platform architecture—e.g., re-architected PlayStation’s upload pipeline to support 500GB–5TB submissions using browser-to-AWS chunked uploads with SNS/SQS and deduplication/resume support.”

“Amazon Alexa engineer who architected and shipped a GenAI Knowledge Agent used by 2M+ customers, focused on making LLM outputs auditable via citations and a verification layer that prevents hallucinations. Built the full vertical slice (FastAPI/LangChain backend + React/TypeScript streaming UI) while keeping p99 latency under 200ms, and has proven incident response experience on AWS (Lambda/DynamoDB scaling issues).”

Osvaldo Calles

Screened

Senior Software Engineer specializing in developer tools, cloud automation, and generative AI

Redmond, WA13y exp

“Built and deployed a production chatbot on osvaldocalles.com and iterated through real-world LLM engineering issues: model quota/cost tradeoffs (migrating to Nova Pro), RAG accuracy via semantic chunking, AWS IAM/guardrail/security pitfalls, and Lambda/API Gateway streaming constraints (prefers JS for streaming layer). Experienced with agent orchestration using Strands SDK (AWS-focused) and LangGraph (Vercel/container deployments), plus evaluation pipelines using LLM-as-evaluator, dashboards, and staged model rollouts.”

Ashi Sinha

Screened

Junior Software Engineer specializing in full-stack and ML/NLP systems

New York City, NY2y exp

“Entry-level full-stack engineer with internship experience at Amazon (Appstore IAP flow + uninstall recommendation workflow) and a health-tech startup (OneVector) where they built a DSUR reporting workflow end-to-end, including document generation, S3-backed versioning/metadata, and secure preview/download. Demonstrates strong production debugging and reliability mindset (instrumentation, deterministic retrieval, idempotent writes) and focuses on UX/performance in high-stakes user flows.”

Gaurav Narasimhan

Screened

Executive AI platform leader specializing in autonomous agent systems for enterprise SaaS

Redwood City, CA26y exp

“Real estate entrepreneur who has previously raised capital from friends and family and has engaged directly with VC firms including A16z. Demonstrates unusually strong founder commitment, having worked two full-time jobs for the past six years while pursuing entrepreneurial goals, with hypothesys.ai described as the outcome.”

Nagsen Dahat

Screened

Executive engineering leader specializing in FinTech platforms and cloud-native systems

New York, NY18y exp

“Motivated by the desire to work independently after spending significant time working for others. Demonstrated notable discipline and follow-through by completing a Master's program with great grades while managing a full-time job, family with two children, and a social life.”

Jared Hoffen

Screened

Senior AI Engineer specializing in LLM agents, RAG, and ML infrastructure

Las Vegas, USA12y exp

“Production-focused AI/ML engineer who has owned LLM agent and RAG systems end-to-end, from experimentation through deployment, monitoring, and iterative optimization. Stands out for building evaluation and observability layers around GenAI systems and delivering measurable gains in task success, regression detection speed, and token efficiency in production.”

Jeremiah Medina

Screened

Senior AI/ML Engineer specializing in LLMs, RAG, and cloud-native MLOps

Orlando, FL11y exp

“Built and owned a real-time clinical AI assistant at Andor Health, taking it from prototype through deployment, monitoring, and iterative improvement. Brings strong healthcare-focused GenAI experience across RAG, hybrid retrieval, LoRA fine-tuning, and production Python services, with measurable gains in accuracy, speed, and reliability.”

“AI/ML engineer with experience spanning Accenture healthcare NLP systems, academic research, and Apple on-device LLM integration. Stands out for owning regulated production pipelines end-to-end—from HIPAA-compliant clinical NLP and EHR integrations to incident prevention, experiment tracking, and optimized on-device inference with LLaMA 3.”

“Engineer with experience at Reddit working on high-scale backend and infrastructure problems, including API redesign for products serving 150M+ daily active users. They also built a production AI agent for automated bug triage with 97% accuracy and substantial time savings, and have hands-on full-stack/AI side-project experience using React, TypeScript, Supabase, and LLMs.”

“Unity/C# gameplay engineer with strong systems architecture depth who has reworked core gameplay ability frameworks, shipped across mobile and standalone VR, and solved multiplayer synchronization issues with server-authoritative netcode. Also brings an unusual crossover into AI tooling, having owned an AI-powered debugging assistant at Arm and integrated LLM workflows into CI/development pipelines.”

“Robotics software engineer with hands-on ROS2 experience building an audio conversion node and integrating Whisper LiveKit for streaming speech-to-text in a simulated hostile (outer space) robot environment. Also worked on a 2023 LiDAR + ML vision obstacle-detection project for a hospital-nurse-assistant robot, and has strong large-scale CI/CD deployment experience from AWS (2022–2024) across alpha/pre-prod/prod stages.”

“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”