Toronto-based mobile engineer with 5 years in fintech, Kotlin/Compose depth, and current code-model evaluation work.
Compose, coroutines, MVVM, release quality.
Payments, lending, trust, reliability.
Evaluation, fine-tuning, model quality.
Kotlin, Compose, Flutter.
Mobile banking, lending, and merchant payments.
LLM evals and model quality.
I build Android applications that people trust with their money. Over the last five years I've shipped production mobile software at Comviva and Rupeek — across mobile banking, merchant payments, and gold-loan product flows — and contributed to the release engineering, performance and security practices those products depend on.
My day-to-day work lives in Kotlin, Jetpack Compose and Flutter, with deep exposure to MVVM, dependency injection, coroutines, and the CI/CD surface around them — Jenkins, Firebase distribution, SonarQube, Checkmarx. I care about codebases that stay readable under pressure and teams that ship without breaking trust.
Since July 2024 I've also been working as an Advanced Coder in AI Training at Outlier, improving code-generation models through prompt design, RLHF-oriented review, testing workflows, and failure analysis across Kotlin, Java, Python, C, and C++ tasks.
I hold an M.Eng in Electrical & Computer Engineering from the University of Windsor and a B.Tech from NIT Jalandhar. I'm based in Toronto and open to senior engineering roles — on-site, hybrid, or remote.
My best work sits inside mobile systems that need clarity, reliability, and thoughtful user-facing execution rather than feature volume alone.
I care about the systems around the code: rollout confidence, crash reduction, review discipline, and the engineering habits that keep teams shipping cleanly.
The AI layer here is practical: code-model evaluation, failure analysis, prompt quality, and an engineering-first sense of what trustworthy AI work actually looks like.
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A systems-first piece on the gap between what AI systems output and how well their builders can actually explain, evaluate, and trust the reasoning underneath. It reads less like trend-chasing content and more like the perspective of someone who has spent real time inside model-quality work.
The tools I reach for every week — grouped by surface. Bolded rows are the primary stack I work in daily; the rest are things I've used seriously on production projects. Year counts reflect hands-on shipping time, not exposure.
I'm currently considering senior Android and applied AI engineering roles — full-time or contract, based in Toronto or remote. If your team is building a meaningful product and this profile aligns with the role, I would be glad to continue the conversation.
Especially strong where mobile architecture, user trust, and delivery quality all matter at once.
Useful on teams introducing AI into customer workflows and needing someone who understands both the product surface and model-quality concerns.
Comfortable with distributed teams, senior-level ownership, and environments where reliability is part of the craft.
Senior Android Engineer based in Toronto with experience across fintech delivery, platform quality, and applied AI evaluation. Best suited to teams that value clear engineering judgment, production trust, and strong product execution.
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