I help teams turn complex workflows into experiences people can understand, trust, and use.
I connect research, psychology, systems thinking and hands-on product building — from enterprise health platforms to AI-assisted workflows and shipped apps.
Three projects show how I move from evidence to decisions, product behaviour and measurable or clearly labelled outcomes.
Search exposed an upstream data problem. I designed a hybrid taxonomy and a human-controlled AI creation flow.
Read Program Creation case study →A 0→1 community product taken from market signals to working Android and responsive-web states.
Read HoodHop case study →A trust-and-access redesign that keeps invitation context visible and reduces mobile effort near live sessions.
Read Participant Sign-up case study →Helped make partner setup roughly 50% faster by simplifying steps, grouping related inputs and reducing guesswork.
Ask about this work →That pause usually means something important: the label is unclear, the next step feels risky, the system is asking too much too soon, or the user is not sure if they can trust what just happened.
If a user has to decode the interface, the design is already doing too much.
AI should help people move forward, but the final choice should still feel like theirs.
Good product design needs repeatable patterns, not one-off screens that break later.
Confidential projects use approved public outcomes. Pre-launch work is labelled clearly, and measurement plans are not presented as achieved impact.
Program name and banner assistance with edit, regenerate, replace, skip and review paths.
Reusable patterns and guidelines so teams do not solve the same problem repeatedly.
Roughly 50% faster setup through clearer steps and grouped decisions.
Designed, built and launched apps, sharpening my scope, QA and release judgement.
In the product, I design boundaries, uncertainty, control and recovery. In my workflow, AI accelerates synthesis, prototyping, build support and QA without replacing product judgement.
Program Creation treats generated names and banners as drafts: users can compare, accept selectively, edit, regenerate, replace, skip and review before anything becomes part of the program.
Define where AI is useful, where it should ask, and where a manual path must remain.
Let people approve, reject, edit or regenerate individual suggestions instead of accepting an opaque bundle.
Distinguish known context from inference and explain what information shaped a suggestion.
Test wrong, incomplete and unavailable outputs so AI failure never traps the user.
I am a Senior UX Designer based in New Delhi, strongest at turning uncertain, high-context workflows into clear product systems. I am looking for a team where research, product strategy, engineering constraints and AI interaction design are treated as one connected problem.
I am open to senior UX, product design, AI product design, design systems and mobile UX roles across global remote teams, company-sponsored opportunities and India-based product teams.