💻 Full-Stack AI Engineer | AI Product Builder | Agentic AI Developer | Machine Learning Enthusiast

I build end-to-end AI products, agents, automation tools, and full-stack applications. I enjoy taking ideas from problem statement to working product — designing the system, building the frontend and backend, integrating AI models and external services, testing failure cases, and deploying the result.

My main interests are AI agents, retrieval-augmented generation (RAG), voice AI, evaluation, reliable backend systems, and developer tools. I care about building products that work beyond the first demo: bounded workflows, validated model outputs, clear failure handling, secure APIs, and measurable results.

📍 Bengaluru, India
🎓 Scaler School of Technology
💼 Open to internships, AI engineering roles, hackathons, and technical collaborations

🌐 LinkedIn
🌐 Kaggle
🌐 GitHub

🧠 Languages
⚙️ Backend & Frameworks
🎨 Frontend
🗄️ Databases
🤖 AI Engineering	LLM Agents · RAG · Vector Search · Tool Calling · Voice AI · Evaluation · Guardrails
☁️ DevOps & Hosting
🧰 Tools

• 🎓 Student at Scaler School of Technology, Bengaluru.
• 💻 Building full-stack AI products, agent systems, automation tools, and developer platforms.
• 🤖 Interested in LLM orchestration, RAG, voice AI, model evaluation, and reliable agent workflows.
• 🧪 I prefer measurable engineering: regression tests, traces, latency checks, validation, and failure analysis.
• 📊 Active on Kaggle, exploring machine learning problems and competition workflows.
• 🚀 Open to AI engineering internships, full-stack roles, hackathons, and product-building collaborations.

• Built an agentic research assistant that plans searches, gathers web and document evidence, and generates traceable citations.
• Created an evaluation harness for answer correctness, citation faithfulness, latency, cost estimates, and execution traces.
• Added bounded tool use, retry handling, evidence deduplication, and explicit failure reporting.
• Stack: Python, FastAPI, React, RAG, LLM agents, evaluation.
• Repository: AI Research Agent

• Built a full-stack application generator with planning, code generation, editing, validation, and automated repair workflows.
• Added structured model outputs, streamed progress, version history, approved file scopes, and sandboxed previews.
• Bounded retries and repair attempts to prevent uncontrolled agent loops.
• Stack: Next.js, React, TypeScript, Supabase, PostgreSQL, Sandpack.
• Repository: Forge

• Designed a multi-tenant voice receptionist for call handling, appointment booking, message capture, and emergency escalation.
• Added deterministic guardrails against invented prices, false bookings, unsupported discounts, and prompt injection.
• Implemented tenant isolation, calendar integration, background jobs, and structured call records.
• Stack: Python, FastAPI, PostgreSQL, WebSockets, Twilio, voice AI.
• Repository: AI Voice Receptionist

• Built and deployed a full-stack e-commerce application with product discovery, authentication, cart, checkout, and order history.
• Added trusted server-side pricing, Stripe webhook verification, idempotent order creation, and user-scoped purchase access.
• Stack: Next.js, TypeScript, Stripe, Clerk, Prisma, PostgreSQL, Vercel.
• Links: Live Demo · Repository

• AI Research Agent — Research agent with web search, document retrieval, citations, traces, and evaluation.
• Forge — AI app builder with bounded generation, code editing, validation, repair, and live previews.
• AI Voice Receptionist — Multi-tenant voice automation platform with booking, escalation, and deterministic guardrails.
• Drift Audio — Full-stack e-commerce experience with Stripe Checkout and persisted order history.
• SmartSearch — Multi-format RAG platform using LangChain, Gemini embeddings, Qdrant, and cited answers. Live demo
• CodeFlux — AI coding platform that generates and runs applications inside E2B cloud sandboxes.
• FieldVoice AI — Voice-first inspection and work-order assistant for field technicians.

• Can build complete MVPs across frontend, backend, database, AI integration, and deployment.
• Comfortable owning AI agents, RAG pipelines, voice interfaces, REST APIs, WebSockets, authentication, and third-party integrations.
• Can contribute to problem framing, rapid prototyping, architecture, implementation, testing, documentation, and final demonstrations.
• Focused on practical solutions with a clear user workflow and a working end-to-end demo.

Building useful AI products from idea to deployment.