Building scalable backend systems and practical AI applications with Python.
I'm a Software Engineer focused on backend development and AI engineering, with hands-on experience building Python-based applications, REST APIs, real-time systems, and LLM-powered workflows.
I enjoy taking a problem from idea → architecture → implementation → testing → deployment.
My current focus is on:
- 🐍 Python backend engineering
- ⚡ FastAPI & Django REST Framework
- 🤖 LLM applications and AI workflows
- 🧠 RAG, embeddings & semantic search
- 👥 Multi-agent systems
- 🔌 REST APIs & WebSockets
- 🗄️ PostgreSQL & database-backed systems
- 🧪 Automated testing & CI/CD
- 🐳 Dockerized applications
RAG · Embeddings · Semantic Search · Prompt Engineering · LLM Evaluation · NLP
These projects represent the kind of engineering and AI work I enjoy building.
Production-style full-stack logistics platform with real-time state streaming and asynchronous processing.
Tech: Django Django REST Framework Django Channels PostgreSQL PostGIS Redis Celery React TypeScript
- Built real-time WebSocket state streaming using Django Channels/Daphne.
- Designed tenant-aware authorization and tenant isolation across API endpoints.
- Used PostgreSQL/PostGIS for database-backed and location-aware workflows.
- Implemented asynchronous processing with Redis and Celery.
- Added unit and integration testing with Pytest.
- Added GitHub Actions CI/CD to catch regressions automatically.
AI-assisted Accounts Payable workflow for extracting, validating and deciding invoice outcomes.
Tech: Python Streamlit Google Gemini OpenAI Pydantic
- Converts invoice PDFs into structured data.
- Uses Gemini/OpenAI for document understanding.
- Separates AI extraction from deterministic business decisioning.
- Validates purchase orders, vendors, currencies and amount tolerances.
- Produces explainable Approved / Review / Rejected decisions.
- Validated with automated tests covering business rules and the end-to-end pipeline.
Full-stack AI platform for conducting mock interviews and evaluating communication performance.
Tech: FastAPI React TypeScript WebSockets PostgreSQL Redis OpenAI MediaPipe
- Built an asynchronous FastAPI backend with React/TypeScript frontend.
- Integrated speech-to-text and AI-based interview evaluation.
- Designed a weighted Interview Readiness Index.
- Evaluates speech pacing, response structure, posture and gaze consistency.
- Uses browser-based MediaPipe processing for computer vision.
- Generates cryptographically verifiable candidate reports.
- Designed the system without server-side video storage.
AI research workflow where specialized agents collaborate to retrieve information and produce structured reports.
Tech: Python CrewAI Google Gemini Tavily Streamlit
- Designed specialized Researcher and Writer agents.
- Implemented agent task delegation and workflow orchestration.
- Integrated Tavily for live information retrieval.
- Designed prompts for structured research output.
- Built an interactive Streamlit interface.
Full-stack support platform with AI-assisted ticket classification and workflow management.
Tech: Django REST Framework React TypeScript PostgreSQL OpenAI
- Built REST APIs for ticket creation, assignment and tracking.
- Integrated AI-powered ticket classification.
- Connected the AI workflow to a React/TypeScript frontend.
- Achieved 92% classification accuracy on the project dataset.
NLP-based resume screening system using embeddings and semantic candidate matching.
Tech: Python Sentence-Transformers Scikit-Learn Streamlit
Local multimodal video summarization pipeline combining transcription, summarization and visual scene detection.
Tech: Python faster-whisper Hugging Face Transformers OpenCV MoviePy
RAG-based question-answering system combining graph retrieval with LLM reasoning and controlled output guardrails.
Tech: Python FastAPI Groq LLaMA NetworkX RAG
Problem
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Understand Requirements
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Data / System Analysis
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Architecture & Design
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Implementation
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├── Backend
├── Database
├── AI / ML
└── Frontend
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Testing
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Docker / CI-CD
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Evaluation
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Improve & Deploy
For AI applications, I pay particular attention to:
Reliability · Evaluation · Explainability · Safety · Performance
I try to build software that is:
- Maintainable — clear structure and separation of responsibilities
- Testable — automated unit and integration tests
- Reliable — validation and controlled failure handling
- Scalable — asynchronous processing where appropriate
- Secure — authorization and tenant isolation
- Observable — meaningful logs and predictable workflows
- Practical — solving a real problem instead of using AI just for the sake of AI
Master of Business Administration (MBA) St. John's College of Engineering & Technology 2024 – 2026
Bachelor of Science — Computer Science Vyshnavi Degree College 2019 – 2023
- Oracle Cloud Infrastructure — Generative AI Professional
- Microsoft Career Essentials in Generative AI
- Google Machine Learning Crash Course
- Career Essentials in GitHub Professional Certificate
- Programming with Python Professional Certificate
I'm interested in opportunities and collaborations around:
Software Engineering · Backend Engineering · AI Engineering · Machine Learning · GenAI · LLM Applications
Build → Test → Learn → Improve