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Vetted Model Monitoring Professionals
Pre-screened and vetted.
Intern Machine Learning Engineer specializing in MLOps and forecasting on AWS
Junior Generative AI/ML Engineer specializing in LLM automation and RAG systems
Mid-level AI/ML Engineer specializing in LLM, RAG, and semantic search systems
Intern Software Engineer specializing in AI/ML and data engineering
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and computer vision
Mid-level AI/ML Engineer specializing in LLM agents, search/recommendation, and MLOps
Mid-level Data Scientist specializing in ML, NLP and forecasting
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level AI/ML Engineer specializing in MLOps, fraud detection, and predictive analytics
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
Junior Generative AI Engineer specializing in LLM fine-tuning and RAG pipelines
Mid-level AI/ML Engineer specializing in Generative AI and cloud MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI/ML Engineer specializing in MLOps, streaming data, and NLP/CV
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.”
Swarag Reddy Pingili
Junior AI/ML Software Engineer specializing in LLM agents and RAG systems
“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”