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Vetted Vector Search Professionals
Pre-screened and vetted.
Mid-level Full-Stack Software Engineer specializing in SaaS, AI/ML, and IoT
Mid-level Full-Stack AI Engineer specializing in LLM automation and scalable APIs
Mid-level AI Engineer specializing in LLMs, RAG, and cloud-native MLOps
Mid-level Software Engineer specializing in Generative AI and backend systems
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Junior Full-Stack Engineer specializing in React/Node and AI-powered web apps
Junior Full-Stack AI Engineer specializing in LLMs and RAG systems
Junior Machine Learning Engineer specializing in AI automation and LLM workflows
Junior Backend Engineer specializing in AI and distributed systems
Senior Machine Learning Engineer specializing in document AI and search systems
Junior Software Engineer specializing in APIs, data pipelines, and LLM/RAG systems
Mid-level Full-Stack Software Engineer specializing in cloud-native AI and enterprise platforms
Mid-Level Full-Stack Software Engineer specializing in scalable systems and GenAI
Mid-level Software Engineer specializing in backend systems for Insurance and Healthcare
Junior Machine Learning Engineer specializing in deep learning and healthcare AI
Junior AI/ML Engineer specializing in RAG and multi-agent LLM systems
Mid-level Full-Stack Engineer specializing in AI products and FinTech
Mid-level AI/ML Engineer specializing in fraud detection and enterprise ML systems
Naveen Kancharla
Mid-level AI & Backend Engineer specializing in RAG systems and scalable APIs
“Built and deployed a production LLM-powered document Q&A system using a strict RAG pipeline (LangChain-style orchestration + FAISS) to help users query large internal document sets. Demonstrates strong reliability focus through hallucination mitigation, curated offline evaluation with grounding checks, and production monitoring (latency/fallback rates) plus stakeholder alignment via demos and business metrics.”
Ruthvika Mamidyala
Mid-level Data Scientist specializing in GenAI, RAG, and predictive modeling
“Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.”