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Vetted Retrieval-Augmented Generation Professionals
Pre-screened and vetted.
Junior AI/LLM Engineer specializing in voice agents, RAG, and robotics systems
Senior Full-Stack Engineer specializing in Python/TypeScript web apps and AI (RAG, agentic workflows)
Junior ML Engineer specializing in search, retrieval, and recommendation systems
Mid-level AI/LLM Application Engineer specializing in RAG, agents, and Python/PyTorch
Entry-level Machine Learning Engineer specializing in LLMs, RAG, and data pipelines
Mid-level Software Engineer specializing in backend systems, data pipelines, and AI/RAG
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Full-Stack Developer specializing in React, Node.js, and AI automation
Junior Generative AI Engineer specializing in LLM systems and RAG
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Mid-Level Full-Stack Software Engineer specializing in AI automation and RAG agents
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”
Jeevan Kumar
Mid-level Full-Stack AI Engineer specializing in LLM systems and RAG
“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”