Vasu Gopal — AI Researcher

AI/ML Engineer & Generative AI Architect with 12+ years of experience delivering enterprise-grade solutions across BFSI, healthcare, energy, and retail. I specialize in building secure, scalable GenAI systems—merging traditional ML, MLOps, and modern LLM-based architectures. My expertise spans supervised/unsupervised learning, reinforcement learning (Contextual Bandits, Q-Learning), statistical modeling (ANOVA, time series), and enterprise-scale deployment of ML pipelines using MLflow, Databricks, and Kubeflow. I’ve led initiatives in churn prediction, demand forecasting, fraud detection, and recommender systems. I’ve led multi-agent GenAI deployments using LangGraph, CrewAI, and AutoGen, integrating retrieval-augmented generation (RAG), hybrid chunking, and memory optimization to power conversational and document intelligence across high-volume pipelines. I've worked across Azure, AWS, and GCP platforms—leveraging OpenAI (GPT-4), Bedrock, and Vertex AI. I design production-grade RAG pipelines with hybrid retrieval (MMR, BM25, rerankers), deploy LLMs using Triton Inference Server & TensorRT, and optimize document retrieval with Pinecone, FAISS, and Neo4j-based knowledge graphs. I’ve built secure AI gateways with PII/PHI guardrails, automated FinOps/SecOps workflows with LLM agents, and implemented CI/CD for real-time AI apps. Recent work includes: Architecting AI gateways with PII filtering and latency <60s for secure LLM access Building multi-agent orchestration with fallback, planner, and knowledge retrievers Engineering knowledge graphs using Neo4j + LangChain for dynamic enterprise Q&A Scaling RAG search pipelines across PDFs, HTML, Markdown using LangGraph + LiteLLM Passionate about pushing GenAI from prototype to production, my approach blends traditional modeling (XGBoost, RL, A/B testing) with modern LLMOps, embedding-based search, and responsible AI design.

Stackforce AI infers this person is a Generative AI Architect with extensive experience in enterprise-grade AI solutions across multiple sectors.

Location: New York City

Experience: 1 yr 2 mos