name : Suresh Sunuguri
location : India
role : Senior Software Engineer → AI/ML Engineer (Transitioning)
experience : 17+ years building enterprise-grade backend systems
strengths :
- High-scale Java & Spring Boot microservices
- Large data systems & event-driven architecture (Kafka)
- Cloud-native AWS infrastructure
- Now applying that depth to AI/ML & LLM applications
currently :
- Learning : Machine Learning, Generative AI, RAG systems
- Building : Real-world AI projects with Python & LLMs
- Goal : Land an AI / ML Engineer role in 2026
open_to : AI Engineer · ML Engineer · Full-Stack AI · Backend + AI hybrid roles| Area | Technologies | Progress |
|---|---|---|
| Machine Learning | scikit-learn · Pandas · NumPy | 🟩🟩🟩🟩🟨⬜ |
| Generative AI | OpenAI APIs · Prompt Engineering | 🟩🟩🟩🟩⬜⬜ |
| RAG Systems | Embeddings · Vector Search · LangChain | 🟩🟩🟩⬜⬜⬜ |
| AI System Design | Architecture · Scalability · MLOps | 🟩🟩🟨⬜⬜⬜ |
Stack : Python · FastAPI · OpenAI API
Feature : Multi-turn conversations with persistent memory
API : RESTful endpoints for chatbot integration
Status : 🔨 In Progress
What it does: A production-ready chatbot backend that remembers conversation context across multiple turns — not just a one-shot Q&A. Built with FastAPI for high-performance async serving and OpenAI's GPT models for natural language understanding.
- Built using **Angular + Spring Boot + Python**
- Stack : Python · FastAPI · OpenAI API
- Supports **Add / Edit / Delete expenses**
- Visual dashboards for:
- 📊 Weekly trends
- 📈 Monthly spending
- Clean UI + REST API integration
Status : ✅ Completed
Stack : Python · scikit-learn · Pandas · NumPy
Feature : Predicts salary based on experience & role signals
Pipeline : Data cleaning → Feature engineering → Model training → API
Status : ✅ Completed
What it does: An end-to-end ML pipeline from raw data to deployed prediction API. Covers the full lifecycle — data preprocessing, feature engineering, model selection, evaluation, and a REST endpoint for real-time predictions.
Stack : Python · LangChain · OpenAI Embeddings · Vector DB (FAISS / Pinecone)
Feature : Upload any PDF → Ask questions in natural language → Get accurate answers
Technique: Retrieval-Augmented Generation (RAG)
Status : 🗓️ Planned — Q2 2026
What it does: Upload a PDF document, ask any question, and get AI-powered answers grounded in the document's actual content — not hallucinated. Uses embeddings and vector search to find the most relevant chunks before sending to the LLM.
| Goal | Target | Status |
|---|---|---|
| 🏗️ Build 5 real-world AI/ML projects | Dec 2026 | 🔄 In Progress (1/5) |
| 🔗 Integrate AI into enterprise Java systems | Q2 2026 | 🗓️ Planned |
| 💼 Transition into an AI Engineer role | Mid 2026 | 🎯 Active Goal |
| 📚 Complete ML specialisation | Q1 2026 | 📖 Studying |
| ☁️ Deploy AI apps on AWS (SageMaker / Lambda) | Q3 2026 | 🗓️ Planned |
17 years of enterprise engineering + AI/ML skills = a rare combination most AI engineers don't have.
✅ I understand distributed systems, Kafka, and microservices at production scale
✅ I know how to integrate AI services into real enterprise backends — not just prototypes
✅ I bring software engineering discipline (testing, clean code, CI/CD) to AI projects
✅ I can bridge the gap between ML models and production-grade Java/Spring systems
✅ I'm not starting from scratch — I'm applying proven engineering depth to a new domain
Looking for AI Engineer · ML Engineer · Full-Stack AI · Backend + AI hybrid roles
💬 "I bring 17 years of enterprise engineering depth to every AI project I build. If you're looking for someone who can ship AI that actually works in production — let's talk."