I build intelligent software systems that combine AI/ML research, LLM applications, backend engineering, data workflows, and production-ready full-stack development.
My work sits at the intersection of:
- Applied AI/ML
- LLM-powered product engineering
- Python backend systems
- Frontend application development
- DevOps, deployment, and scalable infrastructure
I enjoy turning complex ideas into usable products — from AI-assisted workflows and data pipelines to developer tools, web platforms, automation systems, and research-driven software.
I focus on designing and developing systems such as:
- LLM-powered applications for generation, extraction, reasoning, enrichment, and workflow automation
- AI/ML pipelines that bridge experimentation, inference, validation, and production usage
- Full-stack platforms with modern frontend, backend APIs, authentication, and data integration
- Computer vision and multimodal workflows for image understanding, transformation, and assisted decision systems
- Developer and business tooling for internal operations, productivity, and scalable digital products
- Cloud-native and containerized deployments with maintainability, observability, and CI/CD in mind
My technical interests include:
- Applied Artificial Intelligence
- Large Language Models (LLMs)
- Prompt systems and model orchestration
- RAG and retrieval-driven applications
- Structured data extraction and validation
- Automation and agentic workflows
- Backend architecture and API design
- Scalable data and deployment infrastructure
- Research-to-product translation
Python TypeScript JavaScript SQL HTML/CSS
FastAPI Node.js REST APIs Auth Systems WebSockets Async Processing
React Next.js Redux TypeScript Tailwind CSS Streamlit
OpenAI LLM Integrations RAG Prompt Engineering Model Evaluation Structured Extraction Inference Workflows
Pydantic SQLAlchemy Pandas NumPy Requests AsyncIO OpenCV Streamlit
PostgreSQL MySQL MongoDB Supabase Vector-ready data workflows Data modeling & validation
Docker Kubernetes GitHub Actions CI/CD Linux Nginx Cloud deployment Environment management
I’m especially interested in how AI moves beyond demos and becomes useful in real systems.
That includes work around:
- building LLM-backed user experiences
- designing prompt and response pipelines
- grounding outputs with retrieval, validation, and structured schemas
- combining AI services with real application logic
- integrating multimodal capabilities into product workflows
- improving reliability, explainability, and production readiness of AI systems
I care about both sides of the problem:
research awareness and engineering execution
I usually approach systems with a balance of:
- clean architecture
- modular services
- reusable components
- schema-first thinking
- production-aware backend design
- scalable integrations
- developer-friendly workflows
I like building systems that are not only functional, but also:
- maintainable
- extensible
- observable
- easy to iterate on
- AI-powered SaaS products
- Internal platforms and business automation
- Data enrichment and classification systems
- Admin panels and operational dashboards
- E-commerce and catalog-driven systems
- ML-assisted workflow tooling
- Full-stack web applications
- API-first backend platforms
- Research-backed feature development
Right now, I’m most interested in building at the convergence of:
- AI + software engineering
- LLMs + product systems
- Python + automation
- data + deployment
- research + real-world applications
I’m always exploring better ways to combine AI, engineering, and product thinking to build systems that are practical, scalable, and impactful.
Thanks for visiting my profile.