A useful product watchlist gives you work to read and decisions to discuss. These twenty practitioners publish portfolios, case studies or product artifacts that make their thinking visible. Their experience varies: some are moving into product, while others are developing a new public body of AI or independent product work.
Here, “up-and-coming” refers to emerging practice and projects worth following. It is not an age category, a prediction, an award or a claim that every person is early in their career. A portfolio is an invitation to examine a contribution, not a completed employment or outcome audit.
Compare the twenty options
| Specialist |
Relevant focus |
| Jess Lu |
Consumer discovery and AI product design |
| KrishnaS Hemanth |
AI workflows in regulated enterprise software |
| Monish Nallagondalla |
Technical AI product management and public code |
| Haris Rehman |
AI activation and real-world ML constraints |
| Zainab Fasasi |
Marketplace discovery and product documentation |
| Manish Biswas |
Technical AI product work and developer tools |
| Bhramar Raut |
Requirements and role-aware product workflows |
| Lavanya Mathur |
Learning products and workflow simplification |
| Abbas Singapurwala |
Data-quality products and learning concepts |
| Vaibhav Jain |
Early-career research and product requirements |
David Spiegel
 |
Product growth and independent building |
| Aditya Singh |
Technical product building and experiment design |
| Sanjit Kangovi |
AI product building and interactive demonstrations |
| Tamer Osman |
Independent mobile products and monetization |
| Suraj Prakash |
AdTech platform products and AI-assisted building |
| Kevin Astuhuaman |
AI workflow design and human-agent interfaces |
| Vishal S |
Engineering-to-product transition |
| Natally Vinaja |
Cross-functional marketplace MVP practice |
| Sushruth DT |
Product analysis with an analytics foundation |
| Salil Harit |
Backend and product engineering; production commerce |
How we selected this watchlist
We recommend beginning with public work that makes a developing product practice easy to examine: the problem, research, tradeoffs and the person’s actual contribution. Lu’s discovery-to-interface cases, Hemanth’s constrained AI decisions and Nallagondalla’s accessible code lead for that reason. Rehman’s product and technical accounts and Fasasi’s marketplace requirements offer different conversations grounded in visible work.
Experience varies. Later entries include experienced operators publishing new independent projects, prototypes, transition portfolios and an explicitly identified engineering alternative. An independent teardown does not imply employment; a prototype does not establish adoption. This order reflects the usefulness of the published work for a product discussion, not age, predicted career success, immediate availability or a completed employment audit.
Choose the engagement model
For early-career and transition conversations, explore Raut, Mathur, Fasasi, Biswas, Vishal, Singapurwala, Jain and Sushruth. Lu and Vinaja offer consumer and marketplace project reasoning. The technical AI and independent-product work includes Hemanth, Nallagondalla, Kangovi, Rehman, Singh, Osman, Prakash and Astuhuaman. Spiegel and Harit add growth and enterprise delivery experience. These groups describe the published work; confirm the person’s current role and interests directly.
1. Jess Lu: consumer discovery and AI product design
Jess Lu’s PictureCook case study describes product-management work on a children’s AI companion. The most revealing detail is the discovery approach: a physical postcard pilot before the full digital feature. Her account then connects interface simplification with observations about how children used the experience.
Her independent Nibble project, a private video-sharing app, is explicitly labeled a TestFlight MVP. The case study traces research into communication habits through decisions about recording, sharing, and small-group interaction. That gives a hiring manager a tangible consumer-product discussion without treating an early release as a mature business.
Lu is worth following for the connection between research and interface choices. In an interview, explore which observations changed her original idea, which findings were uncertain, and what she would measure after the next release. Her published work provides enough specificity to make those questions productive.
2. KrishnaS Hemanth: AI product judgment under constraints
KrishnaS Hemanth’s portfolio focuses on enterprise software in regulated settings. His AI Quote Automation case study describes narrowing an automation request to extracting information for analysts while preserving human decisions and sign-off. It explains discovery, scope, and his contribution rather than stopping at a screenshot.
A separate case study about a canceled initiative describes recommending that work stop after examining data restrictions and economics. These are his accounts of the projects. The useful hiring signal is the reasoning available for discussion, especially how constraints changed the recommendation.
His work suits a conversation about enterprise AI ownership: where automation ends, who handles an exception, and when a promising feature should be shelved. Ask how the team checked errors after launch and what evidence would have justified revisiting the canceled proposal.
3. Monish Nallagondalla: technical depth with public artifacts
Monish Nallagondalla describes a move from data and AI work into product management. His portfolio covers application workflows, requirements, and delivery alongside technical builds. A useful next step is his public code, which lets an engineering partner inspect more than a list of tools.
The Nirixa repository documents an AI assistant system with persistent memory, multiple interfaces, and evaluation tooling. Readers can inspect the architecture and setup instructions. Repository availability supports a discussion of implementation choices; it does not independently establish reliability or customer adoption.
For a technical product role, ask him to explain one boundary between application logic and model behavior. Which action should require human approval? What happens when context is incomplete? The combination of a product narrative and accessible code makes it possible to examine those decisions together.
4. Haris Rehman: explain the decision behind an AI product change
Haris Rehman’s portfolio spans an Associate Product Manager account for ChatlyAI, computer-vision engineering work and healthcare AI research. The ChatlyAI case centers on persistent conversations and the relationship between early participation and continued use, while the industrial work discusses deployment constraints beyond model accuracy.
The appeal is the opportunity to examine decisions across both product behavior and technical feasibility. An AI feature can be accurate in isolation and still be too slow, inconsistent or awkward to use. His write-ups give readers examples of how those considerations shape the product.
For a hiring discussion, choose one case and ask which evidence changed the proposed solution. Keep the PM role distinct from his engineering and research roles. The reported commercial and usage outcomes are his account; investigate attribution, measurement and the team’s contribution directly rather than treating the figures as a forecast.
5. Zainab Fasasi: make a marketplace idea concrete enough to build
Zainab Fasasi presents an Associate Product Manager portfolio grounded in a quality-assurance background. Her selected work includes BakeSync, an ingredient-pricing and supplier-marketplace case study; HostelHub, a student-housing marketplace; and a Showmax product teardown.
The portfolio is useful for exploring the less glamorous parts of a marketplace: who supplies the information, why buyers should trust it and what a first version must include. The project descriptions connect user research with requirements, user flows and acceptance criteria, giving an interviewer specific material to question.
Treat the case studies and teardown according to their stated format, rather than assuming employment at the brands discussed. Ask Fasasi to distinguish the discovery findings from proposed features and explain which assumption she would test first. Confirm the actual shipped scope and her contribution directly when evaluating a role.
6. Manish Biswas: product reasoning close to the implementation
Manish Biswas’s PM portfolio describes internship work on AI-based CI/CD failure triage and an LLM analytics platform. The accounts connect discovery with SRE and DevOps users to requirements, model thresholds, interface decisions and feedback loops. His main portfolio also links independent AI and full-stack projects.
He is worth following for the connection between a technical system and the user’s decision. A failure-triage tool must help an engineer decide what to do next; generating a plausible explanation is only part of that task. The public descriptions give a hiring manager something more specific to investigate than familiarity with an AI framework.
Ask for a walkthrough of one rejected approach and how user feedback changed the product. Separate internship responsibilities, team results and independent projects. The portfolio supports a technical product conversation, without making every published metric an independently verified outcome.
7. Bhramar Raut: translate operational needs into testable behavior
Bhramar Raut’s portfolio separates his former Wipro Project Engineer role from his Associate Product Manager career direction. The professional account covers hospital-system implementation and product-management support; separate Skillverse and Catalyx projects describe user journeys, requirements and role-based behavior.
That clarity makes the work worth reading. A convincing product requirement needs to say what a particular person can do in a particular state, especially when payment, access and permissions interact. Raut’s project write-ups discuss that work without presenting support responsibilities as independent ownership of a roadmap.
For an early-career product interview, ask him to walk through one requirement that changed after review and the acceptance criteria that followed. This is a candidate portfolio, rather than a consultancy package. Discuss the responsibilities he can take on now and where he needs an experienced product lead’s support.
8. Lavanya Mathur: bring learning and operational context into product
Lavanya Mathur’s portfolio connects a transition into product with experience in learning and talent development and an HR-tech product internship. Her public work includes a workforce-reporting product concept and an explicitly independent UX teardown of NinthMoon’s reproductive-care journey.
Those are useful starting points for evaluating how someone translates a complex process into a clearer experience. The common thread is helping a person understand information and act on it, whether the context is a reporting workflow or a sensitive customer journey.
Read the work as a mixture of developing product practice and self-directed analysis. The healthcare teardown does not establish a client relationship with NinthMoon, and an announced capstone is not a completed delivery. In an interview, ask which requirement she changed after user feedback and how she would test the proposed improvement before investing in implementation.
9. Abbas Singapurwala: make analytical product work understandable
Abbas Singapurwala identifies as an early-career product manager and publishes work spanning SmartDQ, CommuteED and an unread-email summarizer. The SmartDQ case discusses data profiling and remediation, stakeholder interviews, user stories and prioritization for an enterprise data-quality product.
His work is worth reading for the connection between analytical functionality and a user’s workflow. Flagging a data problem is useful only if a person can understand its importance, take the right corrective action and explain the result to someone else. That provides a concrete product conversation beyond a model’s accuracy.
Ask which parts of SmartDQ he owned and which were team decisions. Compare the professional account with a smaller independent prototype to see how he scopes work at different levels. Treat the published percentages and intellectual-property statements as claims to discuss directly, rather than independently established credentials.
10. Vaibhav Jain: make the research question visible
Vaibhav Jain’s portfolio presents early-career product work, including research into uncertainty after job applications, an assisted-banking teardown and a marketplace prototype. The recruiting project connects a small primary-research study with a problem choice, prioritization and a product-requirements document.
The useful quality is that a reader can question the path from observation to recommendation. Were participants concerned about a missing feature, or about not knowing what happened after applying? Those lead to different products. A hiring manager can use that distinction to examine how Jain identifies the problem before proposing a solution.
His published work includes proposals and independent analyses, so keep intended effects separate from measured outcomes. Ask which finding would invalidate the preferred idea and what the smallest test should be. Discuss internship or early-career responsibilities directly, with supervision and decision authority matched to the role.
11. David Spiegel: product growth and independent building
David Spiegel works across product, growth, and design, with a psychology background from NYU focused on human cognition. His interest in how people perceive an experience and decide what to do next connects that background with his product work. His portfolio includes Product Lead experience at 5 Axis Health and independent work on Obeagle, a video-chat product for pets. David Spiegel’s background.
His career account reports 30–50% higher revenue per session from his Content IQ work. That publisher-monetization experience gives context to his AdTech Eval Lab, which turns advertising-operations problems into two synthetic evaluations with defined tasks and deterministic checks. Readers can examine what the exercises ask a system to do and how success is judged. Career background and outcomes.
He belongs on this watchlist as an experienced operator extending his practice through new projects. For a team hiring across growth and AI product work, the interesting connection is between a business problem and a build that makes it testable. Ask David to walk through how he chooses an outcome, scopes the first version, and decides whether the result is useful enough to keep developing.
12. Aditya Singh: show the specification and the working system
Aditya Singh publishes product work including NexusIMS, a multi-tenant inventory system, SafePaste, an on-device privacy tool, and a proposed onboarding experiment for Duolingo. His portfolio places the problem statement and technical implementation alongside the product argument.
The experiment design is particularly useful interview material because it can be examined without pretending the test was run. Ask what decision the proposed variants would resolve, which outcome matters and what adverse effect would stop the experiment. The Duolingo work is an independent design exercise, not evidence that he worked for the company.
For a technical PM role, compare that reasoning with the permissions and workflow choices in his own systems. Discuss what he built, what people actually used and what remains proposed. His published availability is future-dated, so confirm timing directly when discussing a role.
13. Sanjit Kangovi: AI product ideas you can explore
Sanjit Kangovi identifies as an AI product builder. His Verdict demonstration turns fictional advertising data into a marketing brief and clearly labels the dataset as synthetic. It includes different weekly scenarios, giving readers a concrete way to discuss how an AI application should respond when the underlying situation changes.
Another project, Redline, presents a review workflow for AI transcripts through engineering, risk, and business perspectives. Together, the projects make his product ideas accessible through interfaces, rather than requiring a hiring manager to imagine the experience from a presentation.
Kangovi is a relevant person to follow when a team needs someone who can build an exploratory version of an AI workflow. Evaluate him as a product builder. For a PM opening, also examine customer discovery, prioritization, and collaboration; an interactive portfolio alone cannot answer those questions.
14. Tamer Osman: connect a product idea with a usable business model
Tamer Osman’s portfolio describes a computer-engineering background, product work at noon and independent applications. Egypt+, a travel companion, combines location check-ins and exploration mechanics with paid audio tours. Other projects address marketing content from software releases and AI-generated itineraries.
Egypt+ is worth examining because the monetization choice follows a particular use case: a visitor taking a trip. That gives a hiring manager a concrete conversation about when a one-time purchase makes more sense than a recurring subscription, and how the product encourages useful exploration rather than habitual app opening.
Osman publishes freelance product and build services as well as the portfolio. Ask for the product decision he would change after observing real travelers and distinguish his solo project responsibilities from team work at prior employers. Confirm the released scope and operating results directly before assigning a similar project.
15. Suraj Prakash: connect a business workflow with the system underneath
Suraj Prakash presents product and business-analysis work at Moving Walls alongside independently built AI applications. His public cases include a programmatic advertising-platform revamp, CRM and ERP integrations, a reporting tool and an inventory-availability viewer.
He is worth following for work where the product interface depends on operational rules underneath it. An advertising order needs to behave consistently across budgets, auctions, inventory and billing. An apparently simple self-service experience can therefore require substantial product judgment about the underlying system.
This is a practitioner developing a public body of product work, rather than a claim that he is new to professional life. In an interview or consulting discussion, choose one workflow and ask how he handled conflicting stakeholder needs. Keep solo applications, team delivery and independent market research distinct when assessing the contribution.
16. Kevin Astuhuaman: make an AI workflow inspectable by its user
Kevin Astuhuaman’s portfolio combines lending-product experience with newer AI work. The PayPal case is explicitly a validated internship prototype, while Trackly is presented as an independent job-discovery product with human and agent interfaces. Reconstructed screens use sample data.
That separation makes the portfolio useful for evaluating product judgment. A prototype can show how a team investigated a problem without establishing a production rollout. The Trackly account also discusses a concrete failure mode: a poll returning no results can look healthy even when the underlying collection failed.
Consider the work for a technical AI-product conversation, especially around trust, recovery and clarity of system state. Ask which decisions Astuhuaman owned and how he tested whether a user could understand the output. This inclusion recognizes his emerging public AI projects, without implying that an experienced product leader is an entry-level candidate.
17. Vishal S: bring technical tradeoffs into product reasoning
Vishal S’s portfolio describes engineering experience at Zoho in video conferencing and enterprise security, followed by product and strategy study and independent case work. His account distinguishes the formal Member Technical Staff role from the product responsibilities and decisions discussed.
The relevant thread is how technical constraints affect what customers can do. Video quality, bandwidth and reliability are product choices when they change whether people can hold a meeting. His portfolio also lists independent product teardowns and research, which offer a different kind of material for evaluating strategic thinking.
Ask him to explain a tradeoff between user experience and system performance, then compare it with one independent case. Do not infer employment at a company simply because it is the subject of a teardown. For a PM role, discuss discovery, prioritization and commercial decision ownership separately from his demonstrated engineering context.
18. Natally Vinaja: scope collaboration before building a marketplace
Natally Vinaja is credited as PM on Co.Lab’s TeamUp project, a collaboration platform for early-career product, design and development practitioners. The team’s public case describes interviews and surveys, member profiles, project discovery and the challenge of helping people find compatible collaborators.
The project is relevant because its own development required the kind of cross-functional collaboration it proposed to support. The product question was not simply how to publish profiles, but what information a person needs before committing time to a project with unfamiliar teammates.
Treat this as a team-learning and MVP case, without assuming a current employer or production business. Ask Vinaja which discovery findings changed the first version and how responsibilities were divided with designers and engineers. A useful follow-up is what the team deliberately left out, and what member behavior would justify adding it later.
19. Sushruth DT: use analysis to frame a smaller product improvement
Sushruth DT publishes a portfolio combining product case studies with data and machine-learning projects. The Robokiller case proposes a feature set around spam-call blocking, while the Blip Billboards analysis considers customer friction and potential UX improvements in self-service advertising.
Those cases are useful for an early-career product conversation because the interviewer can examine the reasoning without needing to infer a client relationship. The question is how Sushruth moves from a stated problem to a prioritized intervention and a measurement plan, drawing on an analytical background.
Read targets in these studies as proposed goals, not achieved improvements. The page contains older educational and career-positioning information, so confirm current circumstances directly. Ask him to revisit one proposal with what he knows now, explain its weakest assumption and outline the first research or experiment he would run before implementation.
20. Salil Harit: product engineering across enterprise workflows and creator commerce
Salil Harit currently presents himself as a backend and product engineer and explicitly seeks engineering roles. His portfolio connects business discovery and product responsibility to architecture, implementation and operational reliability.
His AstroVista case describes a production content-and-commerce platform where he serves as CTO and lead architect. The account follows creator publishing, duration-based pricing, payments, customer entitlements and purchase recovery. His enterprise fixed-asset case identifies a lead product engineer role covering architecture, backend implementation and user acceptance testing.
He belongs on this watchlist as a product builder whose public work explains consequential technical and commercial decisions. For a PM conversation, examine discovery and prioritization separately from engineering ownership. Ask him to reconstruct a changed requirement or a production purchase failure: what decision did he own, which tradeoff mattered and how did he verify the resulting behavior?
How to turn a portfolio into a useful interview
Start with the problem your team needs solved. Choose one relevant project and ask the practitioner to explain their actual contribution. Separate what they proposed, what they built, what other people owned, and what reached users.
Then examine a difficult decision. A productive discussion covers an alternative that was rejected, the evidence available at the time, and the cost of being wrong. For AI work, include an example where the system should refuse, ask for clarification, or hand control to a person.
Finally, agree on what any claimed result measures. A prototype demonstrates an experience; a live release establishes availability; an outcome claim needs a baseline and measurement context. Use the portfolios above to choose the right conversation, then assess the person against the responsibilities of your role.
Sources
Frequently asked questions
Which up-and-coming product managers and builders should I follow?
This watchlist compares twenty practitioners through public projects and product reasoning. Start with work relevant to your interests: consumer experiences, marketplace discovery, enterprise workflows, growth or technical AI products. Read each case in its stated context and discuss the actual contribution directly.
What does up-and-coming mean in this product-manager list?
It means product practitioners whose emerging projects and published reasoning merit attention. It is not an age category, award, entry-level designation, or prediction of future success. Some already have product leadership experience.
How should a hiring manager evaluate an emerging product manager?
Choose a project relevant to the role and examine the user problem, the candidate's actual contribution, rejected alternatives, and measurement approach. Distinguish a prototype or synthetic demonstration from production work, then discuss the decisions directly with the candidate.
