AI/ML Engineer & Full-Stack Developer. I build intelligent, production-ready applications ā AI agents, ML pipelines, voice-enabled tools, and full-stack systems focused on real-world impact and accessibility.
Iām a Computer Science and Engineering student who builds intelligent systems that solve real problems. From early scripting and automation, I progressed to building voice-based assistants, ML models, and agentic AI workflows. My projects prioritize accessibility, real-world impact, and production-focused engineering.
Fast execution, clean architecture, measurable impact, and delivering intuitive UX for real users ā not just demos.
End-to-end AI agent that generates personalized learning paths using IBM Watsonx Prompt Lab & Foundation Models. Implemented orchestration with Flask backend and a responsive frontend; built iterative prompt pipelines for better personalization.
AI-driven interview simulator delivering context-aware Q&A and structured feedback. Built scoring heuristics, session management, and a user-friendly UI to simulate realistic interview flows.
Voice-enabled assistant focused on automation for everyday tasks. Integrates speech recognition, TTS, and command handlers for quick automation ā designed for accessibility and low-literacy users.
Predictive ML model to estimate offspring thalassemia risk based on parental data. Built robust preprocessing, feature engineering, and evaluation pipelines. Focused on explainability and model validation.
Started with Python, C, and Java. Built small scripts and learned core data-structures and algorithms.
Built initial ML models and desktop automation scripts. Created JarrvisAI ā a voice assistant prototype.
Completed IBM SkillsBuild AI Fundamentals and improved model-building skills. Built Thalassemia prediction pipeline and participated in hackathons.
Completed Google 5-Day AI Agent Intensive Workshop and built LearnMate ā an agentic AI project using IBM Watsonx. Focused on pipelines and deployment-ready systems.
Hands-on agent architecture, prompt engineering, tool chaining, evaluation and deployment. Implemented agent workflows and real use-case prototypes.
Comprehensive course on ML concepts, neural networks, responsible AI and practical model evaluation. Produced extensive course report and exercises.
Built prototypes and pitched AI-driven solutions; focused on accessible UIs and rapid MVP development.
Created voice-enabled accessibility tools and received mentor feedback on usability and technical design.
Built and deployed models, designed pipelines, integrated ML with web services, and focused on explainability & validation. Responsibilities included data preprocessing, model selection, evaluation, and deployment-ready code.
Built frontend/backends using Flask + JS, created APIs for ML services, and implemented UI for non-technical users emphasizing accessibility and clarity.
Open to remote internships and collaborations in AI/ML engineering, data science, and full-stack development.
Email: nnnamitha26@gmail.com
LinkedIn: namitha-r