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Vetted FAISS Professionals
Pre-screened and vetted.
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
Junior Machine Learning Engineer specializing in AI automation and LLM workflows
Mid-level Applied AI Engineer specializing in LLMs, Prompt Engineering, and RAG
Mid-level AI/ML Engineer specializing in Generative AI and cloud MLOps
Mid-level AI Engineer specializing in LLM automation and RAG systems
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
Junior Software Engineer specializing in distributed systems, cloud, and LLM-powered search
Mid-Level Machine Learning Engineer specializing in LLMs and RAG systems
Junior Machine Learning Engineer specializing in deep learning and healthcare AI
Junior AI/ML Engineer specializing in RAG and multi-agent LLM systems
Senior Robotics Software Engineer specializing in C++/Python and ROS2 navigation
Mid-level AI/ML Engineer specializing in MLOps, streaming data, and NLP/CV
Junior Backend/Full-Stack Software Engineer specializing in distributed systems
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM 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.”
sathwik kuchana
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG on AWS
“Built and deployed an LLM-powered clinical decision support and risk monitoring platform for mental health at Valuai.io, emphasizing low-latency, evidence-grounded responses and crisis-safe behavior with clinician escalation. Strong production agent-orchestration background (LangChain/CrewAI) plus rigorous evaluation (clinician-in-the-loop + evaluator agent) and large-scale synthetic testing; also applied multi-agent workflows to document verification and fraud detection during an AI internship at Nixacom.”