Vaibhav is a Senior AI/ML Engineer leading the development of Agentic AI chatbots and autonomous workflow applications using LangChain, LLMs, Deep Learning, RAG, hybrid search, VectorDBs, prompt engineering, FastAPI, LangGraph, and Redis cache. He specializes in generative AI agents, skill analytics platforms, and AI-driven models and is currently collaborating with a Fortune 500 firm to build an automated multi-agent orchestration tool for insights generation for consumption by real estate. With 9+ years of experience spanning finance and technology, Vaibhav brings deep expertise in Agentic RAG systems, LLMs, back-end development, and R&D. His versatile skill set enables him to tackle complex business problems and deliver impactful software solutions.
Led the development of an autonomous agentic AI application for a Fortune 500 Real Estate client, enabling automated generation of insights and pptx-based charts with a human-in-the-loop review process to ensure accuracy, and compliance.
Architected HLD and LLD for AgenticAI applications with long-term memory integration using LangMem, and Redis, enabling scalable storage, retrieval, and contextual grounding of agent interactions.
Monitoring and Observability implementation using Langfuse for tracking, debugging, and improving Agentic-AI applications, by providing structured logs, traces, evaluations, and analytics.
Architected and led the development of a LangChain-powered Generative AI agent for a BFSI client, transforming financial decision-making by automating investor insights and risk analysis.
Designed and implemented an enterprise-grade LLM-based Skill Analytics platform, optimizing HR decision-making by enabling real-time workforce skill mapping.
Developed AI-driven ranking algorithms to analyze employee sentiment from platforms like Glassdoor and Indeed, providing strategic insights for corporate culture transformation.
LangChain
Generative AI
LLM
AI
HR Analytics
SAMSUNG SDS
Senior Data Scientist
2020 - 2023 (3 years)
Remote
Developed and deployed an AI-driven promotion success prediction model using Random Forest and deep learning, improving marketing ROI by 35% through automated predictive analytics.
Led the development of an A/B testing framework, optimizing promotional strategies on the Samsung eCommerce platform, increasing customer engagement by 22% and conversion rates by 18%.
Spearheaded e-commerce AI initiatives, implementing deep learning-powered personalization techniques that enhanced regional consumer preferences and improved market penetration.
Led AI strategy and model development for a Conversational AI chatbot with human-like memory, collaborating with 84 experts across 25 countries. Engineered state-of-the-art NLP pipelines using BERT, T5 models, and Neo4j, enhancing semantic understanding and response accuracy by 30%.
Developed a Deep Learning-based histopathology analysis framework using ResNet50 and Kernelized Weighted Extreme Learning Machines. Achieved accuracy of 92.8% (40x magnification) and 89.98% (200x magnification), improving early cancer detection efforts.
