Neuralgo.ai (goML) | Machine Learning Engineer

• Architect and deploy Generative AI and LLM-powered solutions using RAG, Transformers, and Agentic AI frameworks to solve complex business challenges.
• Fine-tune, optimize, and evaluate LLMs (OpenAI, Llama, Mistral, etc.) for domain-specific tasks, enhancing accuracy and contextual intelligence.
• Build scalable GenAI pipelines integrating LangChain, LlamaIndex, and vector databases (FAISS, Pinecone) for retrieval, reasoning, and orchestration.
• Develop autonomous multi-agent systems enabling intelligent decision-making, task planning, and adaptive learning.
• Implement robust MLOps and DevOps practices using Docker, Git, and AWS for reliable model deployment and lifecycle management.
• Stay at the forefront of LLM, Agentic AI, and Generative AI research, applying emerging techniques to production-ready applications.
• Tools: Python, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Prompt Engineering, LangChain, LlamaIndex, Transformers, Hugging Face, Vector Databases (FAISS, Pinecone), PyTorch, AWS, Docker, Git, MLOps

Infosys Limited | Digital Specialist Engineer (Analytics)

• Collaborated with cross-functional teams to define project requirements, ensuring AI solutions and analytics meet organizational goals.
• Partnered with project managers to identify analytics needs, establish critical metrics and KPIs, and provide actionable insights to drive informed decision-making.
• Designed, developed, and optimized generative AI models to improve performance, scalability, and efficiency in alignment with business objectives.
• Developed and maintained AI and data pipelines, handling data preprocessing, feature extraction, model training, and evaluation to ensure efficient data flow.
• Contributed to best practices for generative AI and data systems, troubleshooting and debugging issues while embracing new technologies to enhance analytical outcomes.
• Tools: Python (Core and Advanced), Machine Learning, NumPy, Pandas, Anaconda, Algorithms, Flask, LLM, Generative AI, Streamlit, RAG, Fine Tuning, Agentic AI, FastAPI

Healthcare Document Intelligence Assistant (RAG-Based)

• Built an AI-driven medical document analysis system using a RAG pipeline with OCR-based text extraction and semantic indexing for multi-format medical files.
• Implemented a FAISS vector database enabling ultra-fast semantic search and evidence-grounded retrieval.
• Developed an LLM-powered QA and report generator (GPT-5) delivering accurate, context-aware answers with source citations.
• Created an automated medical report generator producing structured, professional PDF summaries.
• Integrated Google Drive, secure APIs, session memory, and an intuitive user interface to enable seamless document handling and real-time interactions for healthcare teams.
• Tools: GPT-5, FAISS, RAG, LangChain, Python, OCR, Google Drive

AI-Powered Sales and Site Analytics Chatbot

• Collaborated with clients to gather requirements and design a highly efficient sales analytics chatbot solution, leveraging natural language processing (NLP) for enhanced user interaction.
• Engineered seamless integration of the sales analytics chatbot with Vertex AI and the Gemini LLM model, achieving real-time data processing and improving user query response times by an average of 3 seconds.
• Designed and developed a robust query framework to construct and execute SQL queries, streamlining data retrieval and improving user insights through automated summarization.
• Implemented an NLP pipeline to extract and map user entities, increasing accuracy in query interpretation and conversational responses.
• Converted user queries into SQL statements, transforming data into natural language insights and delivering dynamic visualizations to enhance decision-making in a conversational format.
• Tools: Python, NLP, LLM, Gemini, GCP, Vertex AI, BigQuery, SQL, LangChain

AI-Powered Mortgage Document Understanding and Automation Platform

• Designed and implemented an AI-driven solution leveraging LLMs to automate data extraction and classification from complex mortgage documents within a scalable GCP cloud environment.
• Applied Retrieval-Augmented Generation (RAG) techniques to enhance information retrieval accuracy and streamline data population workflows in the user interface.
• Developed an enterprise-grade Conversational AI chatbot for document question-answering, utilizing LangChain, vector embeddings, and vector databases for efficient semantic search and document understanding.
• Delivered an end-to-end intelligent document processing pipeline, significantly reducing manual data entry efforts and improving operational efficiency.
• Tools: Python, LLMs, RAG, FAISS, LangChain, GCP, Vector Databases

Programming Languages

Python

HTML5

SQL

C

Core Java

Databases

MySQL

PostgreSQL

MongoDB

Core ML & DL Frameworks/Libraries

NumPy

Pandas

Scikit-learn

TensorFlow

Keras

PyTorch

OpenCV

Streamlit

Chainlit

LLM & Generative AI Tools

Transformers

LangChain

CrewAI

LangGraph

LlamaIndex

Autogen

Cloud Platforms

AWS

Azure

GCP

Heroku

Developer Tools & IDEs

Git

VS Code

PyCharm

IntelliJ

Eclipse

Anaconda

Web Frameworks

Flask

FastAPI

Business & Productivity Tools

Power BI

MS Excel

MS Word

PowerPoint

JIRA