Building production-oriented AI systems — retrieval pipelines, backend APIs, document intelligence workflows, and ML infrastructure.
I enjoy building systems that move from experimentation to deployment.
My work focuses on:
→ Retrieval-Augmented Generation (RAG) → Backend architecture & APIs → Semantic search & document intelligence → MLOps and model lifecycle workflows → Scalable AI application design
I care about reliability, observability, and building systems that remain maintainable as they grow.
Upload → Retrieve → Reason → Answer
Built an end-to-end document intelligence platform supporting contextual retrieval and grounded responses.
- Hybrid retrieval (semantic + keyword search)
- Metadata-aware chunking
- Multi-document querying
- Source-grounded responses with citations
- Modular ingestion and retrieval pipeline
Stack
Next.js · Python · Vector DB · LLM APIs
→ Live: https://simplify-ai-lilac.vercel.app/
Reproducible ML workflows from training to deployment.
Building infrastructure for managing ML systems in production.
- Experiment tracking
- Model registry
- Automated evaluation
- CI/CD integration
- Deployment workflows
- Data validation
- Feature engineering pipeline
- Drift monitoring
- Monitoring dashboards
- Canary deployment
Stack
Python · FastAPI · Docker · MLflow
Production-style backend architecture.
REST API designed with maintainability and scalability in mind.
- Layered architecture
- Pagination
- Request validation
- Error handling
- Clean service abstraction
Stack
Java · Spring Boot · PostgreSQL
Convert noisy documents into AI-ready structured content.
Standalone preprocessing engine for efficient LLM ingestion.
- PDF / DOCX extraction
- Markdown conversion
- Metadata tagging
- Adaptive chunking
- Token optimization
Stack
Python · FastAPI · LangChain
Java
Python
TypeScript
SQL
Spring Boot
FastAPI
REST APIs
PostgreSQL
Redis
RAG
Vector Search
LangChain
Embeddings
LLM APIs
Docker
Git
Linux
CI/CD
Vercel
Next.js
React
Tailwind
- Build for maintainability first
- Prefer observability over assumptions
- Measure retrieval quality, not only generation
- Design modular and reusable systems
- Optimize for production constraints
- MLOps and model operations
- Distributed systems
- System design
- LLM evaluation
- Scalable AI infrastructure
Actively looking for opportunities starting mid-2026.
Interested in:
- Backend Engineer
- Software Engineer
- Java Full Stack Engineer
- AI / ML Application Engineer
- Applied AI Systems Engineer
Open to:
✔ Remote ✔ Hybrid ✔ On-site
Portfolio → https://my-portfolio-ten-green-65.vercel.app/
LinkedIn → https://linkedin.com/in/jeswanthjoelalluri
Email → jeswanthjoel8779@gmail.com
IIIT Kottayam · B.Tech CSE · 2026