I am a software engineer focused on building scalable, maintainable, and business-oriented software systems with a strong foundation in backend engineering, full stack product development, clean architecture, database design, and AI-powered automation.
My work sits at the intersection of enterprise SaaS engineering, healthcare technology, AI integrations, and modern web platforms. I build systems that reduce operational complexity, improve workflow visibility, and convert domain-heavy requirements into clean, reliable, and extensible software architecture.
I have hands-on experience designing and developing solutions using .NET Core, Next.js, SQL Server, REST APIs, Clean Architecture, RAG-based AI systems, LLM integrations, automation pipelines, and cloud-ready engineering practices.
My engineering approach is product-driven: I care about performance, maintainability, user experience, operational cost, security, and long-term scalability.
Open To
- Full Stack Software Engineering roles
- Backend Engineering roles
- AI Engineer / AI Integration roles
- SaaS product engineering opportunities
- Open-source collaboration
- Distributed systems and platform engineering work
| Domain | Proficiency | Details |
|---|---|---|
| LLM Integrations | Advanced | Building AI-assisted workflows using LLM APIs, prompt orchestration, structured outputs, and automation pipelines |
| RAG Systems | Advanced | Retrieval-augmented generation with document ingestion, embeddings, vector search, and contextual response generation |
| AI Chatbots | Advanced | Domain-specific assistants for business workflows, support systems, and knowledge-base automation |
| Machine Learning | Intermediate | Model training concepts, classification, prediction workflows, evaluation metrics, and applied ML pipelines |
| NLP | Intermediate | Text extraction, summarization, semantic search, entity extraction, and intent understanding |
| AI Automation | Advanced | Workflow automation using Python, APIs, agents, background jobs, and service orchestration |
| Data Engineering | Intermediate | SQL modeling, ETL-style processing, reporting pipelines, and structured data transformation |
| Computer Vision | Foundational | Image-based use cases, OCR-aware workflows, visual processing concepts, and model integration |
Enterprise Healthcare SaaS Platform
A scalable healthcare-focused SaaS platform designed to support patient workflows, appointments, visits, claims, treatment processes, operational dashboards, and role-based enterprise access.
| Category | Details |
|---|---|
| Stack | .NET Core, SQL Server, Next.js, REST APIs, Clean Architecture |
| Scale | Multi-module healthcare platform with enterprise workflow coverage |
| Performance | Optimized APIs, structured data access, modular backend boundaries |
| Security | Role-based access, authentication flows, audit-aware design |
| Impact | Improves operational visibility, workflow control, and healthcare process automation |
| Repository | Private / Enterprise Project |
The platform focuses on converting complex healthcare operations into maintainable software modules. It emphasizes clean domain separation, scalable database design, reusable API patterns, and a frontend experience built for operational users.
AI-Powered Claim Scrubber & Rule Engine
An intelligent rule-based validation system for healthcare claims, designed to detect claim issues before submission and reduce avoidable rejections.
| Category | Details |
|---|---|
| Stack | .NET Core, SQL Server, Next.js, MongoDB, AI Integrations |
| Scale | Dynamic rule engine supporting multiple validation categories |
| Performance | Real-time validation feedback during claim entry |
| Security | Controlled rule access, structured validation logic, audit-friendly rules |
| Impact | Reduces claim rejection risk and improves billing accuracy |
| Repository | Private / In Development |
The system is designed around configurable business rules, trigger points, and immediate user feedback. It enables billing teams to identify invalid claim conditions early, improving claim quality before external submission.
Inventory Management & Assignment System
A complete inventory workflow system for stock tracking, assignments, returns, consumption, faulty items, and product-level inventory visibility.
| Category | Details |
|---|---|
| Stack | .NET Core, SQL Server, Clean Architecture, REST APIs |
| Scale | Supports stock units, ledgers, assignments, requests, usage, and returns |
| Performance | Ledger-based stock tracking with optimized stock balance reads |
| Security | User-based stock ownership, request approvals, controlled inventory movement |
| Impact | Improves inventory traceability, accountability, and operational control |
| Repository | Private / Enterprise Project |
The system is designed to track inventory from warehouse stock to user assignment and final consumption. It supports practical business cases such as barcode tracking, faulty returns, FIFO-based deduction, and location-based usage records.
YouTube AI Agent Automation Pipeline
A Python-based automation pipeline for YouTube content research, scripting, compliance checking, storyboard planning, video assembly, and upload preparation.
| Category | Details |
|---|---|
| Stack | Python, MoviePy, YouTube Data API, OAuth, OpenClaw, LLM Integrations |
| Scale | Multi-agent pipeline for end-to-end content automation |
| Performance | Modular local agents with provider fallback support |
| Security | OAuth-based upload flow with local token handling |
| Impact | Automates repetitive content production and publishing workflows |
| Repository | Private / Automation Project |
The pipeline combines local agents, API integrations, AI-assisted planning, and real MP4 assembly. It is designed with modularity so each stage can be improved independently without breaking the full workflow.
RAG-Based Knowledge Assistant
A retrieval-augmented AI assistant designed to answer domain-specific questions using internal documents, structured knowledge, and contextual retrieval.
| Category | Details |
|---|---|
| Stack | Python, FastAPI, Vector Search, LLM APIs, Embeddings |
| Scale | Document-driven knowledge assistant architecture |
| Performance | Context-aware retrieval with structured prompt generation |
| Security | Controlled knowledge access and private document processing |
| Impact | Reduces manual lookup time and improves knowledge discovery |
| Repository | Private / AI Project |
The assistant is built around the principle that LLMs should be grounded in relevant source material. It combines document chunking, semantic retrieval, prompt engineering, and answer generation for practical business use cases.
2024 — Present
Building enterprise-grade healthcare and SaaS systems with a focus on backend architecture, frontend delivery, database modeling, workflow automation, and AI-assisted product capabilities.
Scope of Work
- Designed and developed scalable backend APIs using .NET Core
- Built frontend modules using Next.js, React, TypeScript, and Zustand
- Created structured database schemas for healthcare, inventory, claims, visits, and workflow systems
- Implemented business rules, validations, dashboards, and operational workflows
- Worked with AI-powered solutions including chatbots, RAG systems, and LLM-based automation
- Improved software maintainability using clean architecture and modular engineering practices
- Collaborated on product logic, technical planning, and feature-level architecture
| Recognition | Details |
|---|---|
| Enterprise Engineering | Built and contributed to healthcare-focused SaaS workflows and operational systems |
| AI Integration | Developed AI-assisted solutions involving chatbots, RAG workflows, and automation pipelines |
| Backend Architecture | Designed clean, modular APIs and database-backed enterprise features |
| Product Engineering | Translated complex business workflows into usable, scalable software modules |
| Automation | Created workflow automation pipelines using Python, APIs, OAuth, and agent-based design |
Learning:
- Microservices architecture
- API gateways with YARP
- Cloud-native engineering
- Advanced DevOps workflows
- AI agent orchestration
Building:
- Enterprise SaaS platforms
- Healthcare workflow systems
- AI-powered claim validation tools
- RAG-based assistants
- Automation pipelines
Exploring:
- Distributed systems
- Performance optimization
- Cloud cost optimization
- LLM application architecture
- Scalable database design
Open_To:
- Full Stack Engineering
- Backend Engineering
- AI Engineering
- SaaS Product Engineering
- Open Source Collaboration#Connect
Engineering scalable systems, intelligent automation, and product-grade software with clarity, precision, and long-term maintainability.