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Vetted LlamaIndex Professionals
Pre-screened and vetted.
Mid-level ML Engineer specializing in MLOps, data engineering, and GenAI/RAG systems
Mid-level AI/ML Engineer specializing in LLM agents, search/recommendation, and MLOps
Senior Software Engineer specializing in LLM agents and RAG pipelines
Senior AI/ML Engineer specializing in Generative AI, LLMs, and Computer Vision
Mid-level Software Engineer specializing in AI/GenAI and cloud-native backend systems
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level Data Scientist specializing in Healthcare ML and Generative AI
Mid-level AI Engineer specializing in NLP, LLM fine-tuning, and RAG systems
Junior AI/ML Engineer specializing in RAG, multi-agent systems, and enterprise NLP
Mid-level AI/ML Engineer specializing in Generative AI and cloud MLOps
Junior Machine Learning Engineer specializing in LLM agents, knowledge graphs, and multimodal AI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI Engineer specializing in LLM automation and RAG systems
Junior AI/ML Engineer specializing in RAG and multi-agent LLM systems
Mid-level Software Engineer specializing in AI, backend systems, and full-stack development
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.”
Pranava Reddy Kothapally
Junior Data Engineer specializing in Azure, CRM data pipelines, and marketing personalization
“LLM/AI engineer who has deployed production RAG conversational analytics and Text-to-SQL systems over Snowflake and curated data marts, emphasizing enterprise-grade guardrails for accuracy, security, and cost. Notable for a structured approach to reducing hallucinations (curated metric/table registry, SQL validation, RBAC, and citation-backed responses) and for building resilient, observable multi-step agent workflows using LangChain/LlamaIndex and Airflow.”
Phat Dang
Junior Software Engineer specializing in full-stack, DevOps, and GenAI
“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”
Adithya Sharma
Mid-level AI/ML Engineer specializing in MLOps, NLP, and Generative AI
“Built and deployed a production LLM-powered text-to-SQL/document intelligence chatbot on AWS that lets non-technical business users query complex enterprise databases in plain English. Demonstrates deep practical expertise in schema-aware prompting, embeddings-based schema retrieval, SQL safety/validation guardrails, and rigorous offline/online evaluation with human-in-the-loop approvals for risky queries.”
Sree Sai Preetham Nandamuri
Mid-level Data Scientist specializing in Generative AI and LLMOps
“Built a production-grade, semi-automated document recognition and classification system for large volumes of scanned PDFs, starting from little/no labeled data and handling highly variable scan quality. Deployed on AWS using SageMaker + Docker and orchestrated on EKS with a microservices design that scales CPU-heavy OCR separately from GPU inference, with strong reliability controls (validation, fallbacks, retries, readiness probes).”
Yash Pokharna
Intern Software Engineer specializing in backend systems, cloud, and AI agents
“Built and productionized an LLM-based appointment management agent, implementing RAG with Redis and LangGraph plus multi-agent intent handling and rule-based conflict guardrails to prevent double-booking under high load. Experienced in real-time diagnosis of agentic workflow failures using logs/traces and state inspection, and in driving adoption via interactive developer demos and sales-aligned custom customer scenarios.”
Eranda Sooriyarachchi
Mid-level AI Engineer specializing in RAG, conversational AI, and agentic systems
“Built and deployed a production RAG-based clinical decision support assistant at MedLib, focused on fast, trustworthy answers from large medical documents. Demonstrates deep practical experience improving retrieval accuracy (semantic chunking + metadata-aware search), controlling hallucinations with grounded generation and thresholds, and adding clinician-requested citations using chunk metadata, with evaluation driven by healthcare professional review.”