Product designer with 4+ years of experience turning complex problems into AI-powered products people love.
Reimagined the candidate onboarding journey, smoothing first-time setup so seekers reach their first meaningful action faster and with less drop-off.
Turned an AI screening SKU into an embedded hiring workflow — making AI explainable evidence recruiters can act on, not a black-box verdict, while keeping them in control of every decision.
Redesigned the job search results page into a clearer, personalized experience with Match Score insights and richer job details.
Fixed a fragmented seeker profile experience to improve usability and drive more frequent, seamless profile updates.
Designed a source-agnostic, enterprise-grade AI hiring platform that screens and interviews candidates across every sourcing channel — built solo from concept to launch, with humans kept in control of decisions.
"Preeti is born curious and has been very crucial in defining and building user experience at Foundit. She has an eye for detail for the work. She is a go-getter and asks the right questions. She is really quick with iteration and absorbing feedback."
"Preeti began her career at Foundit, successfully transitioning from an intern to a full-time role. Over her two-year journey, I witnessed her remarkable growth, which gave us the confidence to entrust her with multiple key projects. She consistently delivered outstanding results with minimal guidance, handling them single-handedly."
"Had the privilege of working with Preeti at the start of her UI/UX design journey. Her rapid growth and dedication were evident from day one. She has evolved into a skilled designer, showcasing a keen eye for user-centric solutions. Her passion and work ethic make her a standout professional in the field."
"Preeti is one heck of a hardworking youngster in Foundit's Product Design team. I have literally seen her exponential growth curve before my eyes — from intern to full-time, driven by sheer passion, hard work, and curiosity to improve. She constantly comes up with new ideas and never shies away from a healthy debate."
"Preeti is one of those people you can trust with a problem and be confident it will be driven to a thoughtful, high-quality outcome. At Apna, she consistently demonstrated strong ownership, handled complex initiatives independently."
"Worked with Preeti at Apna and honestly she's one of the best designers I've collaborated with. Her design and product thinking is really sharp — she never rushes to solutions. She digs deep into the problem first, asks the right questions, and challenges assumptions before touching Figma."
View the chapter on my interests, stories
and life outside of work.
I'm a Product Designer currently designing AI products at Apna, owning end-to-end experiences across candidate and recruiter platforms. I love exploring how design can tell stories, spark emotion, and make everyday moments a little more meaningful.
With 4+ years of experience, my work spans UX audits, research, critique, and prototyping — turning complex problems into intuitive, thoughtful experiences.
My journey started with a love for painting and sketching, and that curiosity still drives how I approach design today — with creativity, empathy, and a genuine interest in people.
Away from the screen, you'll usually find me watching movies, painting, sketching, café hopping, or getting lost in a good series — always collecting little bits of inspiration along the way.
Currently based in Bengaluru, India. If you're building something with heart, let's talk!
My story so far is about learning to see people, patterns, and possibilities, and using design to turn that understanding into something that makes a difference. ✦
Digital experiences that feel effortless, considered, and genuinely useful.
Redesigned the existing job search results page, where seekers view job listings and job descriptions, for both mobile and desktop platforms.
About Foundit: Formerly known as Monster, Foundit is a talent management platform that not only facilitates connections between job seekers and companies but also offers services to enhance candidate skills. It also offers comprehensive solutions for businesses, facilitating the discovery and recruitment of top talent.
Foundit users face several challenges that hinder their job search experience and diminish engagement:
The primary objective of redesigning the job search results page is to create a streamlined, personalized, and user-friendly experience that addresses current issues of cluttered layout, lack of personalization, broken UX, and insufficient job information. By enhancing visual clarity, incorporating smart insights like a Match Score, and ensuring comprehensive and user-friendly job details, we aimed to significantly improve user satisfaction, engagement, and provide more job applications after the revamp.
Drivers:
Key Metrics:
We began by segmenting the job search result page into three parts: job cards, job descriptions (JD), and filters. Our initial focus was on enhancing the job card section.
Current Job Card Analysis — The current job card included: Job Title, Company Name, Job Type, Job Location, Years of Experience, Salary, Skills, Date Posted, Tag (e.g., Great Place to Work), Save Icon, GPTW Banner.
Initial Questions and Considerations:
To understand how we can enhance the job card experience, we conducted comprehensive user research using a mixed-methods approach including Surveys, In-depth Interviews, and Usability Testing.
Key Findings:
The revamped job card includes: Job Title, Location, Experience, Estimated Salary, Company Logo, Smart Tags (Early Applicant, Quick Apply, High Match), Job Posting Date, and Save Job Feature.
After Redesign: The redesigned job card for the same position maintains the essential details but presents them in a more streamlined and visually appealing manner:
After finalizing the job card, we proceeded to redesign the job description (JD) section. Initial analysis revealed job titles lacked prominence, job descriptions were presented in long paragraphs making it challenging for users to scan, and overall UI improvements were needed.
JD Research Key Findings:
The existing filter functionality was riddled with inconsistencies, creating confusion for users. Many available filters were underutilized by job seekers. Our goal was to redesign the filter functionality to align with industry standards, simplifying the job application process and enhancing user experience.
Revamp of the existing seeker profile page of Foundit (formerly Monster.com) to improve usability and significantly boost job seeker engagement, encouraging more frequent and seamless profile updates.
About Foundit: Formerly known as Monster, Foundit is a talent management platform that not only facilitates connections between job seekers and companies but also offers services to enhance candidate skills. It also offers comprehensive solutions for businesses, facilitating the discovery and recruitment of top talent.
The current seeker profile isn't user-friendly and has a fragmented, broken experience. This issue is adversely affecting the frequency of profile updates.
By revamping the profile page, we aimed to improve usability and significantly boost job seeker engagement, encouraging more frequent and seamless profile updates. Our goal was to create an enhanced and intuitive user experience that increases user interaction and satisfaction.
Current M-site and App experience showing the pain points:
Participants: 6 users, mixed gender, age 22–34 years. We conducted moderated usability testing over 1 week.
Tasks given to participants:
Priyanka Sharma, 32, Finance Manager — Main Goal: Secure a role with ample growth opportunities. Pain points: Annoyed by excessive pop-ups disrupting smooth profile navigation. Believes the profile score is not easily visible. Finds prominent banners distracting, mistaking them for ads. Finds it difficult to navigate between different sections.
Ansh Thakur, 25, Software Engineer — Main Goal: Obtain a senior engineering position at a leading tech company. Pain points: Frustrated by numerous pop-ups that interfere with seamless profile navigation. Finds the navigation between profile sections cumbersome and time-consuming. Notices inconsistencies in the design that make the user experience less professional.
Avg traffic on profile page (Sep 23 – Jan 24):
The user feedback analysis highlights crucial redesign priorities for the job seeker profile page. Streamlining intrusive pop-ups, prioritizing seamless mobile access, and enhancing profile score visibility are focal points. Addressing banner inconsistencies and emphasizing the work experience section align with user preferences. The redesign will integrate robust resume parsing, clear prompts, and early skills placement in the visual hierarchy. Introducing multiple resumes and refining job recommendations aim to accommodate diverse needs. The overarching goal is a user-friendly, visually cohesive profile page tailored to the unique preferences of job seekers in India, fostering a positive and efficient user experience.
A source-agnostic, enterprise-grade AI hiring platform that screens, interviews, and coordinates candidates across every sourcing channel, while keeping humans in control of the final decision. Built solo from concept to launch.
Enterprise hiring teams handle huge volumes of candidates from many channels at once: job portals, ATS, databases, referrals. OnlyRounds is a source-agnostic, enterprise-grade AI screening and interviewing platform that lets teams screen, interview, and coordinate candidates consistently using AI agents, no matter where those candidates came from.
What it is not: not limited to apna-sourced candidates, not a single static chatbot, and not a hiring-decision engine. Final calls always stay with humans.
apna's earlier AI screening product (AISKU) worked well for SMBs because apna was their primary sourcing channel. For enterprises, apna contributed only a fraction of total sourcing, so most candidates still fell back to slow, manual screening and the ROI of automation collapsed.
Design a platform that unifies all sourcing channels into one pipeline, lets recruiters configure trustworthy AI agents for screening and interviewing, and orchestrates outreach at enterprise scale, all without taking the hiring decision out of human hands.
JD ingestion, structured job creation, interview-round setup, and evaluation-criteria editing.
Configuring voice/video agents, language, persona, and reviewable scripts, with a test-before-go-live step.
Multiple entry points converging into one pipeline, with distinct inbound and outbound (consent-first) experiences.
Status tracking, recordings, AI summaries, and credit/usage reporting for the workspace.
Recruiters paste, upload, or AI-generate a JD; an LLM extracts and cleans it into a structured job that becomes the source of truth. They then set up interview rounds (screening, interview, or human-led tasks), edit AI-generated evaluation criteria, configure each agent (voice/video, language, persona), and test the agent before going live.
A single source-of-truth JD plus editable, AI-generated criteria makes setup fast without giving up recruiter control, and the test step builds trust before a real candidate is ever called.
Candidates enter via bulk upload, embedded apply links, direct interview links, or auto-sourcing from ATS and job platforms, and all of them land in a single pipeline per job. Inbound candidates (who have intent) go straight into screening with a short intro; outbound candidates (contacted from a database) get an interest check and give consent first. The screening criteria stay identical across both; only the entry script changes.
Keeping evaluation criteria constant while varying only the entry script gives fair, comparable results across sources, and consent-first outbound respects candidates who never asked to be contacted.
An orchestration engine coordinates multi-channel outreach, deciding when to contact a candidate, through which channel (call, WhatsApp, email), for what objective (interest, screening, interview, scheduling), and what to do on response or silence. It optimises for time-of-day and honours retry limits and night-time policies. The design principle: agents decide what task; the workflow decides when and how.
Separating "what" (agents) from "when/how" (workflow) kept the system understandable to configure and dramatically lifted reach; automated coordination hit candidates at the right moment on the right channel.
After each interaction, the data is scored by an evaluation agent against the recruiter's criteria and resolved to a clear status: Fit, Not Fit, Partial Fit, Not Interested, No Response, or In-Progress. In the recruiter portal, teams see status at a glance, listen to recordings or watch videos, and read AI-generated summaries, plus credit and usage reporting for the workspace.
A tight set of well-defined statuses plus one-click access to the recording and summary lets recruiters act in seconds, and keeps the final judgment human, backed by the actual evidence.
Letting agents own the task and the workflow own timing and channel kept an inherently complex system configurable and legible.
Every source has a different candidate state; the UI had to normalise them into one pipeline without hiding what mattered.
Editable criteria, test-before-go-live, recordings, and summaries let recruiters hand work to AI while keeping the decision, and the accountability, human.
Outbound candidates never asked to be contacted, so an interest-check-first flow wasn't a nicety; it was the design.
Recruiters hiring frontline roles receive hundreds, even thousands, of applications per job. AI Recruiter interviews candidates, evaluates answers against recruiter-defined criteria, summarises every conversation, and recommends qualified candidates before any human interaction. It was never meant to replace recruiters. It removes repetitive first-round screening while recruiters keep complete control of the final decision.
The shipped product in action: AI Agent evaluation inside the recruiter dashboard.
apna is India's largest hiring platform for frontline roles: telecalling, delivery, customer support, sales. Volume is the defining problem. Every candidate still needs manual screening, repetitive qualification checks, and follow-up calls before a recruiter can identify the right fit.
AI Hiring Assistant exists so that hours of daily calling stop delaying hiring. The AI conducts the first screening interview automatically. Recruiters open the dashboard to qualified, explained recommendations instead of a raw pile of applications.
Pricing and Monetization was owned by the Monetization team. I collaborated closely with them, along with Product Managers, Engineers, AI teams and Business stakeholders, through research, design, validation and launch.
This chapter covers the journey up to Iteration 2. Launch results and Phase 2 come later.
Recruiters hiring at scale repeatedly told us the same thing:
Instead of helping recruiters screen candidates faster, what if the first screening round was already completed before recruiters even opened the dashboard? That became the foundation of AI Recruiter.
They wanted it to remove repetitive work while giving them enough evidence and control to make the final decision.
Help recruiters use AI screening to make faster hiring decisions while preserving trust, control, and confidence?
The legacy flow, end to end, from checkout to candidate list:
The recruiter journey
The candidate journey
Mapping this made one thing obvious: AI Recruiter was not a single feature. It touched multiple products across the hiring ecosystem.
Introduce AI Recruiter as an add-on while making its value immediately understandable.
Help recruiters configure interview criteria with minimal effort.
Present AI recommendations in a way recruiters trust and can confidently act upon.
An interview flow that feels conversational rather than intimidating.
Rather than attempting to perfect the experience immediately, we built a set of initial concepts covering all four touchpoints. The goal was not visual polish. The goal was learning, before investing heavily in development.
These became our first hypotheses. The early explorations below became our Phase 1 prototype.
Hypothesis: recruiters will pay for AI at checkout if the value is obvious at the moment of posting a job.
We slotted a Smart-AI tier beside Classic and Premium, priced above Premium, with an inline demo so recruiters could experience a screening call before paying. What we expected to learn: whether checkout is where conviction happens.
The first pricing concept: a third AI tier with a demo interview entry, straight into checkout.
The demo: recruiters talk to the AI before they buy it.
Problem: configuration effort kills adoption. Assumption: if AI drafts the questions from the job details, recruiters only need to review, mark deal-breakers, and continue.
Questions pre-drafted by AI from the job post. Deal-breaker toggles make criteria decisive.
Full control on tap: answer types, preferred answers, remove.
Setup opens right after the job goes live, while intent is high.
Expected behaviour: recruiters accept most AI questions, edit a few, and finish setup in minutes.
The riskiest questions lived here. Cards or tables? Depth or speed? Rather than commit, we explored four dashboard concepts and let the trade-offs surface. No final direction yet. The purpose was divergent thinking.
Rich cards: maximum transparency per candidate.
Table: maximum scanning speed.
Master-detail: list beside full AI evidence.
Filter-first: control for high-volume pipelines.
Approach: conversational, not intimidating. The AI screening call reached candidates right after applying, with a simple in-call layout: duration, mic and volume controls, nothing to learn.
Iteration 1: the call arrives immediately after applying, while intent is hot.
We wanted completion and comfort. Whether an instant call delivers either was exactly what testing had to answer.
With Iteration 1 in front of recruiters, we needed evidence, not opinions. We wanted to understand how recruiters actually screened, what information they trusted, which UI elements they ignored, whether AI influenced decisions, and where friction lived.
Insight: AI was not convincing as a standalone purchase; recruiters wanted to experience it first. Problem: the demo lived too deep in checkout. Change: the AI card moved to the centre with a Recommended tag, value props on the card, and the AI recruiter introduced on the dashboard itself, before posting, with a walkthrough and real voice clips. Expected impact: higher demo engagement and premium attachment.
The AI tier moves centre. Framing does the selling.
Value props and the voice waveform, on the card itself.
Meet your AI recruiter, before you even post.
The walkthrough, plus selectable pre-recorded voices.
Insight: recruiters said setup felt like configuring an employee. They wanted to hear the voice and control the company pitch. Problem: Iteration 1 read as a form. Change: voice avatars in English, Hindi and regional languages, an editable company pitch, and AI-generated questions to review. Expected impact: setup completion up, drop-offs down.
Pick a voice, shape the pitch, review the questions.
Progressive feedback while the agent gets ready.
Insight: hidden insights were ignored; five signals drove every decision. Problem: reasoning sat two clicks deep. Change: an AI Agent Fit tab in the pipeline, with the evaluation, criteria checks and key signals visible directly on the candidate, and recordings one tap away as the tie-breaker. Expected impact: faster shortlists, higher trust in Fit.
The direction after testing: a fast list, AI evaluation at the surface, proof one tap away.
Insight: instant calls made candidates anxious; they wanted preparation time. Problem: the interview started the moment they applied. Change: a transition state, "your interview is about to begin", with preparation cues before the AI connects. Expected impact: higher completion, lower drop-off.
Iteration 2 shipped. What follows is what six months of live numbers told us.
Every number below comes from the live metrics tracker, from launch week through the end of January.
And the numbers that told us the truth:
We read the data and the complaints case by case. Five fixes shipped while Phase 1 was still live.
Mid-interview drop-offs died as "inconclusive". Restarting meant repeating everything.
No-response climbed toward 35 to 44%.
Candidates with several AI applications could not tell the calls apart.
Some recruiters wanted only top matches, others a broad pool.
Shortlists stayed at 4 to 6%. Recruiters verified everything.