PROBLEM FIRST, THEN THE RIGHT TOOL
I am a software engineer who likes getting to the heart of a problem before reaching for a tool. My work spans full-stack product development, applied AI and machine learning, data-intensive systems, and cloud and platform engineering, because the application, the data, the models, and the infrastructure all have to work together in the end.
Product Engineering
End to End
Interfaces, application logic, and the services behind them, built as one working product rather than separate layers.
Applied AI
In Production
Retrieval, orchestration, and evaluation designed to live inside real systems, with grounding and fallbacks that hold up.
Data-Intensive Work
Real Inputs
Ingestion, indexing, and analysis over messy, heterogeneous data instead of clean sample sets.
Platform & Reliability
Built To Operate
Containers, pipelines, and observability so a system stays maintainable long after the first release.
Featured Projects
Each project highlights technical depth, implementation choices, and practical outcomes across intelligent systems, machine learning, and full-stack development.
AI AGENT + RAG SYSTEM
INTELLIGENT-LOAN-APPROVAL-AGENT.MDA decision-support system for loan workflows that combines multi-step LLM orchestration, retrieval, and structured knowledge grounding.
HEALTHCARE ML APP
LIVER-CIRRHOSIS-STAGE-PREDICTION.MDA live machine learning application that predicts liver cirrhosis stage through a simple web interface built for accessible clinical-style risk assessment.
FINANCE + EXPLAINABLE ML
RISK-SCORING-FOR-LOAN-APPROVAL.MDAn explainable ML workflow for loan approval that combines structured preprocessing, risk scoring, and business-facing decision support.
ANDROID + ML APP
WASTE-RECOGNITION-APP.MDAn Android application that helps users identify waste items and improve disposal decisions through ML-backed recognition and guided flows.
NLP + DEEP LEARNING
ADVANCED-SENTIMENT-ANALYSIS-USING-BERT-AND-LSTM.MDA comparative NLP project that fine-tunes BERT and benchmarks it against deep learning baselines for sentiment classification.
NLP + EXPLAINABLE CLASSIFICATION
FAKE-NEWS-CLASSIFICATION-WITH-LLM-TECHNIQUES.MDA text-classification project focused on fake/real news detection using multiple NLP representations with explainability and class-balance handling.
Skills
A representative view rather than a fixed boundary. Most of this was picked up because a project needed it, and the specifics change with the problem.
languages.ts
backend.services.ts
frontend.product.tsx
ai-ml.workflows.ts
data.storage.sql
cloud.platform.yml
About
Choosing the architecture that fits, understanding what each trade-off actually costs, and building something useful, reliable, and worth maintaining.
how.i.work
I like getting to the heart of a problem before reaching for a tool. Sometimes the right answer is sophisticated. Sometimes it is simple. What matters is the judgment to tell the difference, and the willingness to be honest about what each choice costs later.
engineering.scope
My work spans full-stack product development, applied AI and machine learning, data-intensive systems, and cloud and platform engineering. I enjoy operating across those areas because real products rarely stay inside one of them. The application, the data, the models, the infrastructure, and the experience all have to work together before any of it counts.
how.i.learn
Curiosity shapes how I work. I like stepping into unfamiliar domains, learning the fundamentals, finding the constraints that are actually real, and testing assumptions before committing to a direction. Making something work is only the starting point. I want to know why it works, where it would fail, and what would make it better.
clarity.matters
Teaching and mentoring sharpened something I keep coming back to: clarity. A complex idea should still be explainable, and technical depth becomes far more useful the moment it can be shared, questioned, and improved with other people.
outside.engineering
Competitive chess has been part of my life for years. Pattern recognition, calculation, patience, adapting when the position changes, and the habit of looking past the obvious move — more of that carries into engineering than I expected it to.
Contact
Exploring full-time software engineering roles across product development, AI and machine learning, data-intensive systems, and platform work.
contact.form