Vetted Large Language Models (LLMs) Professionals

Pre-screened and vetted.

AP

Senior AI/ML Engineer specializing in computer vision, GenAI, and 3D spatial analytics

Baltimore, MD12y exp
Under ArmourNorth Carolina State University
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MS

Senior Machine Learning Engineer specializing in AI, NLP, computer vision, and GenAI

Somerville, NJ9y exp
NICEHamdard University
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MV

Mid-level AI Engineer specializing in healthcare and financial ML systems

5y exp
Blue Cross Blue Shield AssociationUniversity of Massachusetts Amherst
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SA

Mid-level Software Engineer specializing in full-stack FinTech and AI systems

New York, USA5y exp
Fidelity InvestmentsStevens Institute of Technology
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JH

Entry-level data analyst specializing in social science and nonprofit analytics

Hanover, NH1y exp
Dartmouth CollegeDartmouth College
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JH

Senior AI/ML Engineer specializing in Generative AI and conversational systems

San Diego, CA14y exp
SeqsterUSC
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SR

Senior Software Engineer specializing in cloud-native distributed systems and FinTech

Nashville, TN8y exp
OracleNorthwest Missouri State University
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JB

Senior software engineer specializing in cloud support, cybersecurity, and SCADA systems

17y exp
AmazonEverest University Orlando South
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HK

Senior Full-Stack .NET Engineer specializing in cloud, APIs, and data platforms

Fremont, CA15y exp
VanguardUniversity of Mumbai
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NM

Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics

4y exp
New York Life
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SP

Mid-level Full-Stack Engineer specializing in AI-powered enterprise applications

US6y exp
BioNTech
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AM

Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines

Philadelphia, PA11y exp
CoverMyMeds
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RS

Mid-level GenAI/ML Engineer specializing in LLMs, RAG, and agentic AI

Warrensburg, MO4y exp
Costco
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AJ

Anshul Joshi

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in distributed systems and GenAI

Austin, TX4y exp
University of Texas at AustinUniversity of Texas at Austin

Capgemini engineer with 4+ years building and deploying high-availability, low-latency fraud detection APIs and multi-cluster distributed systems for a Fortune 20 bank, including zero-downtime production rollouts and multi-layer (SQL/network/hardware) performance debugging. Also built a Python + OpenAI/LangChain LLM-powered grading workflow for Austin School for Women, cutting feedback time from 90 minutes to 5 minutes per submission for 200+ learners.

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NV

Nirav Vibhakar

Screened ReferencesStrong rec.

Executive technology leader specializing in healthcare IT, cloud, and cybersecurity

Hanover, MN11y exp
GPN TechnologiesUniversity of Wales Trinity Saint David

Current CTO of an analytics company in the eye industry that was acquired by a larger organization, and now exploring building a new analytics platform for individual provider offices. Focused on helping practices grow revenue and improve the patient journey to drive long-term retention, with entrepreneurial motivation tied to building a legacy business for their family.

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TW

Senior Machine Learning Engineer specializing in AI systems, LLMs, and MLOps

San Francisco, CA14y exp
SiftUniversity of Central Florida
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Trevor Peace - Senior Full-Stack Engineer specializing in Healthcare IT in San Francisco, CA

Trevor Peace

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in Healthcare IT

San Francisco, CA27y exp
CareHiveCarleton College

Founding/sole engineer who has built multiple healthcare products from scratch, spanning medical billing, cardiology triage, and cardiac device monitoring. Particularly compelling for healthtech and AI roles: they combine full-stack TypeScript/AWS execution with strong product judgment in regulated settings, including shipping voice-enabled RAG workflows and rejecting unsafe AI automation when simpler human-in-the-loop UX was the right answer.

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DC

dezhou chen

Screened

Mid-level AI Engineer specializing in LLM systems and GenAI products

Remote, USA4y exp
StealthUniversity of Illinois Urbana-Champaign

AI-focused product engineer working on LLM routing, prompt engineering, and multimodal API integrations at production scale. They describe improving system accuracy, latency, and token usage, fine-tuning an internal model to reduce third-party API dependence, and adding safety guardrails through prompt-injection testing and red-team evaluation.

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