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Vetted Vector Databases Professionals
Pre-screened and vetted.
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/ML Engineer specializing in fraud detection, credit risk, and NLP/RAG
Mid-level AI/LLM Application Engineer specializing in RAG, agents, and Python/PyTorch
Mid-level Full-Stack AI Engineer specializing in web and generative AI solutions
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Full-Stack Developer specializing in React, Node.js, and AI automation
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
Junior AI Engineer specializing in production RAG systems and GPU-accelerated inference
Mid-level Full-Stack AI Engineer specializing in agentic AI and RAG systems
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).”
“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.”
Harshit Vashisth
Junior AI Engineer specializing in LLMs, RAG systems, and MLOps
“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps
Junior Software/AI Engineer specializing in LLM agents and RAG systems
Iaroslav Kuznetsov
Mid-level Machine Learning Engineer specializing in AdTech and scalable data systems
“Built and scaled an internal AI code-search/assistant agent that expanded from engineering-only to broader internal users, tackling legacy code and inconsistent standards to make a RAG pipeline production-ready. Uses a metrics-driven approach (user feedback + automated Python evaluation for retrieval relevance and latency) and has handled high-pressure outages, including moving parts of the stack off AWS and adopting Milvus on internal infrastructure for resilience.”