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Vetted GPT-4 Professionals
Pre-screened and vetted.
Mid-level AI/ML Researcher specializing in LLMs, conversational agents, and health NLP
Mid-level AI/ML Engineer specializing in GenAI, NLP, and AWS MLOps
Senior AI/ML Engineer specializing in LLMs, RAG, and product-driven AI systems
Senior Full-Stack Software Engineer specializing in AI-enabled microservices and micro-frontends
Mid-Level Full-Stack Software Developer specializing in Financial Services
Mid-level Machine Learning & AI Engineer specializing in LLMOps, digital twins, and RL
Junior Software Engineer specializing in backend, cloud, and LLM applications
Junior Software Engineer specializing in distributed systems and ML computer vision
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG for healthcare
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Senior Unity & Full-Stack Engineer specializing in VR/XR and multiplayer systems
Mid-level Business Analyst specializing in BI, predictive analytics, and operations
Senior Cloud DevOps & Security Engineer specializing in AWS/Azure, Kubernetes, and compliance
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Shamala Chandrappa
Mid-level AI Engineer specializing in agentic AI, LLM systems, and healthcare AI
“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”
revanth polthi
Mid-level Data Scientist specializing in Generative AI and MLOps
“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”
Mid-level Data Scientist & AI Engineer specializing in NLP, computer vision, and MLOps