// about me
Building AI systems that
solve real problems
I'm an AI Software Engineer with hands-on experience designing and deploying LLM-powered applications and agentic AI systems in production environments.
My work spans the full AI stack — from RAG pipeline design and vector retrieval optimization, to FastAPI backend development and containerized cloud deployments. I've diagnosed production OOM crashes, built multi-system n8n automations, and architected MCP-based tool registries that decouple reasoning from tooling.
Engineering rigor matters as much as AI knowledge.
Agentic AI Systems
Design multi-node LangGraph pipelines with stateful memory, conditional branching, and production-grade error handling.
MCP Architecture
Implement Model Context Protocol to decouple tool definitions from agent reasoning — enabling dynamic, extensible tool registries.
RAG Pipelines
Architect FAISS-backed retrieval systems with tuned chunk sizes, embedding overlap, and memory-optimized fallback strategies.
Production Deployment
Containerize full-stack AI apps with Docker, deploy to Render, and orchestrate automated workflows via n8n.
// technical skills
Full-stack AI engineering
Agentic systems, multi-node pipelines, and LLM orchestration.
// featured projects
Things I've built & shipped
Each project is a production AI system — not a tutorial, not a demo. Real problems, real architecture decisions, real deployments.
AdOps Intelligence Agent
AI-powered campaign monitoring and optimization platform
Problem
Ad operations teams waste hours manually analyzing campaign KPIs, identifying underperformers, and writing optimization recommendations across dozens of campaigns.
Solution
A fully automated agentic AI pipeline: upload a CSV to Google Drive → n8n triggers analysis → LangGraph orchestrates 3 nodes → Groq LLM reasons over RAG-retrieved strategies → Gmail delivers recommendations in <2 minutes.
// system flow
MediBot MCP
Multimodal AI healthcare assistant using Model Context Protocol
Problem
Healthcare information access is fragmented — symptom analysis, medication identification, and multilingual support each require separate tools or expensive proprietary systems.
Solution
A unified multimodal AI agent using FastMCP and Groq Llama 3.3 70B. MCP decouples tool definitions from agent reasoning, enabling dynamic tool selection. Vision LLM identifies medications from photos. gTTS delivers responses in English and Telugu.
// mcp architecture
Smart Assistive Identifier
Real-time object detection and face recognition — 25+ FPS on CPU
Problem
Assistive technology for visually impaired users is either expensive, requires internet connectivity, or fails to run on modest hardware without GPU acceleration.
Solution
An offline, self-contained executable combining YOLOv8 object detection and face_recognition library at 25+ FPS on CPU only. Confidence thresholds and frame sampling rates were tuned for throughput. Multilingual voice alerts in Telugu and English via pyttsx3. Dark-mode Tkinter GUI.
// pipeline
Heart Disease Prediction
Django ML web app with 88%+ cross-validated accuracy
Problem
Clinical decision support tools are either too complex for small clinics or lack the transparency needed for medical use — black-box models with no explainability.
Solution
A Django-backed ML web application with full EDA → feature selection → ensemble modeling (Logistic Regression, KNN, Random Forest) → REST API pipeline. 88%+ cross-validated accuracy on clinical datasets.
// pipeline
// work experience
Where I've worked
AI & ML Engineer Trainee
Innomatics Research Labs
Hyderabad, India
Designed and deployed structured LLM workflows for document summarization, entity extraction, and automated reporting — implementing Pydantic output validation schemas to enforce response reliability — across production AI pipelines.
Built and evaluated RAG (Retrieval-Augmented Generation) pipelines for domain-specific Q&A over structured datasets — combining embedding-based retrieval with LLM reasoning — to reduce hallucinations and improve answer accuracy.
Developed agentic AI task automation scripts using LLM function-calling and tool-use patterns — enabling autonomous multi-step execution across data ingestion, transformation, and reporting workflows — with zero manual intervention.
Operationalized LLM and ML model outputs as FastAPI REST endpoints with async request handling, Pydantic schema validation, and structured error responses — integrating AI inference into downstream business pipelines.
Trained, fine-tuned, and cross-validated ML and DL models (classification, regression, clustering, neural networks) through systematic hyperparameter tuning and feature engineering — achieving 85%+ accuracy on held-out validation sets.
Designed ETL pipelines and feature engineering workflows (Pandas, NumPy) to prepare high-quality training data for LLM fine-tuning and ML model development — reducing manual data processing effort by 50%.
// education
B.Tech — Computer Science & Engineering
AI & ML Specialization
SVIT (JNTUH), Hyderabad · 2021–2025
ML · DSA · OS · DBMS · Software Engineering · Computer Networks
// certifications
Data Science with AI/ML, GenAI & Data Analytics
Innomatics Research Labs · 120+ hrs · 2025
Machine Learning Internship with Python
Verzeo Edutech
MERN Full Stack Web Development
NxtWave · In Progress
// let's connect
Let's Build the
Future of AI
Together.
Actively seeking roles where engineering rigor matters as much as AI knowledge. Open to remote and on-site opportunities.