AI Engineer with 10+ years of experience at the intersection of artificial intelligence, research, and full-stack development. Currently architecting advanced AI systems at Eternal Me AI, specializing in RAG systems, LLMs, MLOps, and agentic AI. Master’s in Computer Science with AI specialization from UMass Dartmouth.
Unique engineering profile combining production AI/ML systems, full-stack development, and cloud architecture. Passionate about building intelligent systems that learn, adapt, and deliver real-world impact. Published IEEE researcher on structured deep neural networks and patent inventor for digital brain technology (US 19/382,448).
Technical Skills
AI & Machine Learning
Programming Languages
AI Infrastructure
Cloud & DevOps
Frontend & Backend
Data Science & Analytics
Professional Experience
Eternal Me AI
Senior AI Engineer
- Architected production RAG system with AWS Bedrock, Sagemaker, LangGraph, and PostgreSQL, implementing Model Context Protocol (MCP) for seamless integration with external tools, enabling personalized AI digital twins with dynamic context retrieval
- Developed advanced NLP pipeline for intelligent fact extraction with semantic deduplication and entity recognition
- Engineered serverless architecture on AWS Lambda handling real-time AI interactions with fast response times
- Implemented embedding-based memory system using OpenAI Ada-002 for context-aware conversations and personality modeling
- Created responsive web interface with real-time voice recording, progressive web app features, and mobile-optimized UX using Next.js and TypeScript
Yottaa Inc
Senior Software Engineer
- Developed AI-driven test generation system using machine learning models. Analyzing UI code changes and end-user behavior patterns to automatically generate intelligent test scenarios, significantly reducing testing effort and improving coverage.
- Design responsive UI components using React and JavaScript
- Architect automated test frameworks for web applications and APIs
- Lead dev productivity ops and drive code quality through technical reviews and agile practices
Kiwi Technologies Inc
Software Engineer - Drone
- Built microservice architecture for real-time data processing from drone-mounted radio systems
- Developed internal support application using Quickbase platform, streamlining ops workflows
- Collaborated on unit test development and implemented bug fixes to improve code quality
VUI Inc
Software Engineer - Conversational AI
- Developed interactive dashboards for visualizing conversational AI performance metrics
- Identified algorithm inaccuracies and improved ML model performance
- Led test planning and execution for ML-powered chatbots across mobile, web, and telephony
Common Sensing Inc
Senior Engineer - Data Science & AI
- Designed and implemented deep neural networks using TensorFlow and Keras to predict insulin dose amounts from sensor data, achieving high accuracy for the GoCap smart insulin cap
- Developed real-time monitoring dashboard using HTML/JavaScript/CSS that collects and visualizes data from Bluetooth-enabled medical devices
- Created comprehensive data analytics reports using Python, Pandas, NumPy, and Matplotlib to analyze patient usage patterns and device performance metrics
- Single-handedly architected and built comprehensive test automation frameworks from ground up, including mobile app testing (iOS/Android) with Java/Appium, RESTful API testing with Java/JUnit, and firmware testing via custom Bluetooth Low Energy web app
- Led quality assurance initiatives for FDA-regulated medical device software, creating detailed test documentation and execution reports while training and supervising technicians in testing procedures
Dunkin' Brands Group Inc
Software Engineer (Contract)
- Built desktop application for automating API queries and streamlining data retrieval workflows
- Developed comprehensive test scenarios and test cases for mobile application testing
Intuit QuickBase
Software Engineer
- Built responsive frontend analytics interface with RESTful API integrations connecting UI to backend database systems
- Engineered automated test frameworks and CI/CD pipelines covering web, API, and database layers
- Developed comprehensive database testing solutions to validate data integrity and query performance across relational databases
University of Massachusetts Dartmouth
Teaching Assistant - Agile Development & Java
- Mentored 50+ senior students in agile software development methodologies and Java programming
- Provided technical guidance on Java programming assignments and projects
- Facilitated adoption of best practices in software development lifecycle
Education
MS in Computer and Information Science
Specialization: Artificial Intelligence
University of Massachusetts Dartmouth
2016 | GPA: 3.73/4.00
BS in Computer and Information Science
University of Pune, India
2012
Patents and Publications
System and Method for Creating and Evolving a Comprehensive Digital Representation of Human Intelligence
US Patent Application 19/382,448 - Amol Gade et al.
Liquid Measurement Systems, Apparatus, and Methods Optimized with Temperature Sensing
US Patent 10255991B2 (Apr 2019) - Key technical contributor developing artificial intelligence models for dose measurement accuracy and comprehensive testing protocols for the GoCap smart insulin cap system
Smart Real Estate Assessments Using Structured Deep Neural Networks
IEEE SCI 2017, San Francisco
Key Projects
AI-Powered Skin Cancer Detection
CNN using TensorFlow/Keras
Developed a convolutional neural network model for automated skin cancer detection and classification, achieving high accuracy in distinguishing between benign and malignant lesions.
Data Visualization Platform
D3.js, JavaScript, HTML/CSS
Built an interactive productivity analytics dashboard using D3.js, enabling real-time visualization of complex datasets with dynamic charts and customizable reporting features.
NLP - Sentiment Analysis System
Python, NLP, Machine Learning
Created a sentiment analysis tool for Amazon product reviews, implementing natural language processing techniques to extract insights and predict customer satisfaction scores.
AI Test Agent
Python, LLMs, RAG, Embeddings, Vector Databases, Agentic AI, GitHub Actions
Building an agentic AI testing platform that intelligently generates, executes, and analyzes software test scenarios. Implements a RAG pipeline with embeddings and vector databases to retrieve relevant code context, enabling LLMs to produce accurate, coverage-optimized test cases. Agentic AI orchestration autonomously reasons through edge cases, adapts to code changes, and iterates on failing tests. CI/CD integration via GitHub Actions enables fully automated test generation on every push, reducing manual QA effort and improving reliability at scale.