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Vetted scikit-learn Professionals
Pre-screened and vetted.
Mid-level Full-Stack Developer specializing in React, Node.js, and AI automation
Mid-level Database Developer specializing in SQL, ETL, and cloud data platforms
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Entry-Level Software Engineer specializing in healthcare data and AI-enabled tools
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Intern Full-Stack Software Engineer specializing in Node.js, AWS, and scalable backend systems
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Entry-Level Full-Stack Engineer specializing in backend APIs and cloud architectures
Mid-level AI & Data Science professional specializing in MLOps, deep learning, and UAV research
Junior Full-Stack Developer specializing in Django/React and cloud-native APIs
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).”
Gabe Hoing
Intern-level software engineer specializing in full-stack and data systems
“Built an AI agent management system in a senior design project to support cybersecurity analysts with gathering and triaging emerging threat intelligence from sources like CISA. Stands out for a thoughtful, production-minded approach to AI development, using specialized agents, strict output schemas, and deterministic controls to manage failure cases.”
“Full-stack AI engineer who has built and deployed multiple end-to-end LLM products, including an AI interview assistant, a multi-agent market research platform, and a policy document explainer. Particularly strong in productionizing agentic workflows, integrating tools like Whisper, Tavus, LiveKit, CrewAI, and LangGraph, and hardening messy real-world AI/document pipelines with validation, memory isolation, and fallback handling.”
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.”