Software engineering · AI/ML · Quality automation
I build software that turns complex problems into useful, testable experiences — from AI-driven tools to automation that makes products more dependable.
I’m interested in the details behind software that works: the data, interfaces, system boundaries, tests, and decisions that shape a reliable product.
I learn by building, measuring what happens, and revisiting decisions that do not hold up. My work spans AI/ML prototypes, web applications, and quality engineering — with a growing focus on automation and responsible implementation.
I value clarity over complexity: a useful project should explain the problem, what I contributed, why the approach was chosen, and what still needs work.
“Good engineering is not only making something work. It is making its behaviour understandable and dependable.”
Selected work across AI/ML, problem-solving, and interactive learning. Each project links to its live experience or source repository so you can inspect the implementation.
Exploring how web interfaces can be inspected for dark patterns using browser automation and text-based signals.
A personalized learning platform project focused on structuring learning paths and tracking learner progress.
A structured data-structures and algorithms archive with a browser-based problem explorer, solutions across languages and platforms, and focused topic notes.
A tabular reinforcement-learning implementation that makes the learning loop explicit: exploration, temporal-difference updates, policy extraction, and success-rate evaluation.
A commodity-price prediction project exploring time-series approaches for agricultural data.
Organizing application test cases around user flows, realistic data, boundary conditions, and repeatable regression checks.
Technical credibility comes from explaining the reasoning behind an implementation — including limits and failed approaches.
Use compact diagrams to explain components, data flow, trust boundaries, and where failures can occur.
Document alternatives considered, constraints, the chosen approach, and what you would change with more time.
Link to source, test commands, screenshots, benchmark methodology, and the specific result each artifact supports.
A concise selection of technologies from my project work and learning. This is not a claim of equal proficiency across every tool.
Python, C++, JavaScript, SQL
Pandas, NumPy, scikit-learn ecosystem, NLP and time-series experimentation
HTML, CSS, Node.js, Flask, FastAPI, React fundamentals
MySQL, MongoDB, PostgreSQL fundamentals
Selenium, test case design, boundary testing, regression thinking
Git, GitHub, Linux/WSL, browser devtools
Quality evidence should show what was tested, under which conditions, what failed, and what changed — not just that a tool was used.
This static portfolio does not need a database, login, tracking scripts, or a contact form. Keeping the attack surface small is intentional.
A selection of completed learning and practical problem-solving work.
Data structures and algorithms practice, supported by a public repository of organized study material and solutions.
Explore DSA repository ↗Credential links can be added when their public verification URLs are available.
Reading, doodle art, cycling, and boxing are personal interests that make life broader than a list of technologies.
I enjoy reading about personal development, sketching and doodle art, and finding ways to be more consistent with practice. I’m interested in the process as much as the outcome: small experiments, reflection, and steady improvement.
AI-assisted workflows, practical software testing, dependable web experiences, and how to explain engineering decisions clearly.
This is a manually maintained snapshot, not a live status feed.
Let’s connect
I’m interested in software engineering, AI/ML, quality automation, and collaborative projects where careful thinking matters.