Keerthi T
Generative AI Full-Stack Developer | LLMs | RAG Pipelines | LangChain | Hugging Face | Cloud | Python | React
- Role
- Ai Full Stack Developer at Experian
- Location
- Kent, OH, US
- LinkedIn followers
- 500 followers
About Keerthi T
Building the Future with Generative AI & Full-Stack Innovation Ever wondered…
Experience
Ai Full Stack Developer
Jun 2024 — Present · US
Developed and deployed full-stack applications integrating LLMs and generative AI models for chatbots, content generation, and summarization using React, Node.js, and Python.• Designed and implemented RESTful and GraphQL APIs to serve AI/ML models, including RAG pipelines for knowledge retrieval.• Fine-tuned and integrated GPT and Transformer models into web applications to enable intelligent user interactions.• Built AI-driven features such as automated code generation, personalized recommendations, and retrieval-augmented content delivery.• Leveraged LangChain, Hugging Face, OpenAI APIs, and vector databases to create context-aware AI agents.• Developed real-time messaging and conversational interfaces powered by LLMs for enhanced user engagement.• Optimized backend AI inference pipelines for LLMs and RAG workflows using Docker, Kubernetes, and cloud platforms.• Created interactive dashboards and visualizations to monitor AI and RAG system performance using Plotly, D3.js, and Power BI.• Implemented scalable microservices architecture to support AI model deployments in production.• Conducted unit testing, integration testing, and model validation to ensure reliability of AI applications.• Implemented user authentication and authorization with JWT and OAuth 2.0 in AI-powered web platforms.• Collaborated in Agile/Scrum teams to deliver AI-enhanced products with intelligent full-stack solutions.• Integrated search and knowledge retrieval systems with AI models to provide contextual responses.• Researched and implemented emerging generative AI techniques to continuously improve product features.• Streamlined CI/CD pipelines for AI applications to enable faster deployment and model updates
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