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Vetted Multi-Agent Systems Professionals
Pre-screened and vetted.
Mid-Level AI/Full-Stack Engineer specializing in conversational AI and SaaS products
Mid-level Full-Stack Engineer specializing in web platforms and AI-enabled products
Intern Data Scientist specializing in LLM agents, RAG, and real-time ML pipelines
Mid-level AI/ML Engineer specializing in LLM automation and data ingestion systems
Junior Machine Learning Engineer specializing in LLMs and multimodal AI
Mid-level GenAI Engineer specializing in AI agents and FinTech platforms
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Junior ML Engineer specializing in search, retrieval, and recommendation systems
Mid-level AI/LLM Application Engineer specializing in RAG, agents, and Python/PyTorch
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic systems
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 Full-Stack AI Engineer specializing in agentic AI and RAG systems
Entry-level AI/ML Engineer specializing in RAG and conversational AI
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.”