Sachin Jain

Product · Systems · Applied AI

Sachin Jain

Technical Product Manager

Jack of all trades, Masters of Software Engineering. Noodles... don’t noodles. Products don’t build themselves, and there is no secret ingredient. Yesterday is a retrospective, tomorrow is sitting in the backlog, and today is the sprint. I like to get close to the work, understand how the pieces fit together, try things for myself, and keep learning as I go. Sometimes the plan changes, sometimes the answer shows up halfway through the work, but that is usually where the interesting part begins. Those who can, do. Those who can’t, plan.

The Experiences that shaped how I work
Work

Product ownership across problems, systems, delivery, and outcomes.

Move a work item from the career backlog into focus. One chapter at a time.

Focus
Chapter 01Jan 2026 — Present

Rangbheeni

Product&TechnologyConsultant·ProBono

Turningtextileupcycling,women’slivelihoods,andclimateimpactintoadigitalplatformpeoplecanunderstandandengagewith.

01Overview

Rangbheenitransformspre-lovedclothingintonewproductswhilecreatinglivelihoodopportunitiesformarginalizedwomen.Myworkfocusesonthedigitalproductthatcommunicatesthatmodelandsupportstheorganization’soutreach.

02Outcomes/Delivered
01

EstablishedalivedigitalplatformforRangbheeni’sproducts,stories,events,andorganizationaloutreach.

02

Launchedacontent-groundedAIassistantthathelpsvisitorsfindapprovedinformationabouttheorganization,products,andevents.

03

Createdamaintainablefoundationforfrequentlychangingcontentwhilekeepingtheplatformpracticalforasmallnonprofittooperate.

04

Continuetoownproductprioritiesandproductionreleasesastheorganization’sdigitalneedsevolve.

RangbheeniProduct&TechnologyConsultant·ProBonoTurningtextileupcycling,women’slivelihoods,andclimateimpactintoadigitalplatformpeoplecanunderstandandengagewith.OverviewRangbheenitransformspre-lovedclothingintonewproductswhilecreatinglivelihoodopportunitiesformarginalizedwomen.Myworkfocusesonthedigitalproductthatcommunicatesthatmodelandsupportstheorganization’soutreach.Outcomes/DeliveredEstablishedalivedigitalplatformforRangbheeni’sproducts,stories,events,andorganizationaloutreach.Launchedacontent-groundedAIassistantthathelpsvisitorsfindapprovedinformationabouttheorganization,products,andevents.Createdamaintainablefoundationforfrequentlychangingcontentwhilekeepingtheplatformpracticalforasmallnonprofittooperate.Continuetoownproductprioritiesandproductionreleasesastheorganization’sdigitalneedsevolve.Product RoadmapStakeholder CollaborationDigital ExperienceContent StrategyAI EnablementRelease PlanningUBS/CreditSuisseProductManager·AssociateDirectorEvolvedaglobalpost-tradeplatformaroundtheoperationalproblems,clientneeds,andsystemdependenciesthatmatteredmost.OverviewOwnedtheroadmapandend-to-enddeliveryofaglobalswapspost-tradeplatformsupportingmillionsofdailytransactions,whiledrivingthetransitionfromlegacyarchitecturetowardscalablemicroservicesandimprovingresilience,operationalefficiency,andclient-facingcapabilities.Outcomes/DeliveredReducedrecurringcash-breakinvestigationandturnaroundfromnearlyaweektominutesbyreshapingtheoperationalexperiencearoundasingle-actiondashboard.ExpandedtheplatformwithpositionnettingtomeetanurgentTier1institutional-clientrequirement,thenevolvedthecapabilitythroughsubsequentreleases.Extendedconfigurabletrade-allocation,lot-depletion,andunwindcapabilitiesacrossworkflowsspanninginternalsystems,vendorplatforms,andmanualprocesses.Transitionedtheplatformtowardaleanermaintenance-statearchitecturefollowingtheUBSacquisition,consolidatingsixserversacrosstwodatacenterstothreeinonewhilepreservingrequiredperformanceandoperationalstability.Backlog ManagementRequirements ManagementMVP DevelopmentSDLCAgileRelease PlanningEnterprise SystemsCloud ModernizationAI EnablementPlatform ModernizationMicroservices ArchitectureService FabricCarnegieMellonUniversityProjectManager&Developer·GraduateCapstoneDeliveringaconfigurablereal-timestream-analysisframeworkthatcouldsustainhighthroughputwithoutdataloss.OverviewGraduatecapstoneworkcombiningprojectdeliveryandhands-onengineeringforconfigurablereal-timestreamanalysis.Outcomes/DeliveredBuiltaconfigurableApacheStormframeworksupportingdatapartitioningandwindowingacrossreal-timeanalysisworkloads.Validatedsustainedthroughputof2.5GBperminutewithoutdataloss.Project DeliveryReal-Time SystemsStreaming DataSystem DesignPerformance ValidationApache StormCreditSuisseTechnologyAnalyst·PrimeFinanceBuildingtheengineeringfoundationbehindpricing,dividendprocessing,andcompositionmanagementforindexproducts.OverviewWorkedasleadC#developeronaPrimeFinanceplatformmanagingindexsecuritiesandcustombaskets.Outcomes/DeliveredDeliveredplatformcapabilitiessupportingpricinganddividendprocessingforindexsecuritiesandcustombaskets.Supportedcompositionmanagementforcustombasketswithinthesameproductionplatform.BuiltthesystemsandworkflowfoundationthatlatershapedhowIapproachtechnicalproductdecisions.Software EngineeringPrime FinanceEnterprise SystemsC#Pricing WorkflowsProduction SupportPRODUCT OWNERPRODUCT MANAGEMENTBACKLOGROADMAPPRIORITIZATIONREQUIREMENTSUSER NEEDSSTAKEHOLDERSDELIVERYRELEASESMVPDEPENDENCIESRISKSACCEPTANCE CRITERIAWORKFLOWSOPERATIONSENGINEERINGQAPLATFORMSSYSTEMSADOPTIONOUTCOMESDISCOVERYEXECUTIONTRADE-OFFSFEEDBACKITERATIONVALUE
Then I started building to learn faster
Built to Understand

I started building to understand what AI changes in a product.

Independent products became a way to test product decisions directly: what deserves automation, what needs memory, where structure helps, and where AI should stay out of the way.

Immersive Yatra

AI-powered road trip planner for building detailed multi-day travel plans with structure, flexibility, and practical routing in mind.

AI ApplicationTravel PlanningPrompt EngineeringOpenAI APIStructured OutputsWorkflow Design
Explore

Problem

The challenge was to create something that organize routes, timing, and stops into a single flow but leave room for personalization.

Product Choice

User shares the trip intent and system shall transform it into structured, multi-day planning with more useful pacing, organization, and itinerary detail but still leave a room for user's own customization.

What It Proved

Transforms rough travel intent into structured day-by-day planning.

Balances route flow, trip pacing, and richer itinerary detail.

Built as a usable AI product experience, not just a prompt wrapper.

What I Learned

AI outputs improve when the prompt aligns with intent rather than over-specifying behavior. Too much constraint reduces usefulness, while the right level of openness improves outcomes.

Prep Room

Prep Room is an AI-powered interview preparation system designed to help users build clear, structured, and credible answers through guided workflows rather than one-off responses.

AI ProductInterview PreparationTechnical Product ManagementWorkflow DesignSystem ThinkingPrompt Engineering
Coach
Playbook
Highlights
Skills

Problem

Interview preparation is usually fragmented across notes, documents, and generic chat threads. That makes answers harder to refine and harder to reuse.

Product Choice

Users work through answers, save stronger material, and build a structured base of moments and playbooks that can be reused across interview scenarios.

What It Proved

Refines interview answers through iterative coaching instead of one-shot generation.

Captures career moments and reusable answer material in one evolving system.

Reduces context switching between notes, chat, and preparation documents.

What I Learned

The product became a good test of how AI feels when it is embedded into a workflow instead of acting like a single detached chat box. Contract-based communication improves reliability between agents

Personal Research Agent

A focused research assistant designed to synthesize insights across selected papers and material

AI ApplicationResearch AssistantRAGDocument RetrievalContext EngineeringInformation Synthesis
Step_01
Query
Step_02
Retrieve
Step_03
Rank
Step_04
Response

Problem

Research material is scattered across multiple papers and it is time-consuming to extract and connect key insights.

Product Choice

Documents are processed and stored in a FAISS vector database. Relevant sections are identified based on the user query. Retrieved content is processed to generate clear, grounded outputs

What It Proved

Supports retrieval, ranking, and synthesis over private source material.

Designed around deeper research workflows rather than simple Q&A.

Represents a stronger AI-systems layer in the portfolio.

What I Learned

Chunking strategy and token usage must be balanced — too little context reduces quality, while too much increases cost without better results.

WatchThis

A shared media catalog and personal watchlist designed to reduce duplicate data, make title resolution more reliable, and turn the catalog into a reusable product foundation.

Product ArchitectureTechnical Product ManagementAI Product DesignAPI DesignData ModelingSystem Design
Shared
Catalog
CAST
YEAR
GENRE
DIRECTOR
WATCH
RESOLVE
GUESS

Problem

A personal watchlist becomes harder to maintain as titles, people, genres, credits, and metadata are repeatedly duplicated across users and features. The product needed a shared foundation that could support watchlists, search, title resolution, and new experiences without turning every action into an external API or AI call.

Product Choice

WatchThis maintains a shared media catalog and links users to their own watch state. Existing catalog data is reused first, external resolution is used only when needed, and authenticated natural-language requests can use AI. The same catalog powers a public guessing game with structured clues and filters.

What It Proved

Evolved duplicated user-owned media records into a normalized shared catalog that can support multiple product experiences.

Created resolution paths that reuse existing data and cheaper external sources before invoking an LLM.

Extended the catalog into a public movie-guessing experience while protecting a low-resource personal service from unnecessary cost and abuse.

What I Learned

A stronger shared data model unlocked more product possibilities than adding isolated features. It also reinforced that AI should be one resolution path among several, not the default path for every request.

CareerTracker

A local-first Windows application for managing the full job-application workflow without turning personal career data into another hosted SaaS account.

Product ManagementTechnical Product ManagementWorkflow DesignLocal-First ArchitectureAI Product DesignData Modeling
Local Workspace
01
Company
02
Role
03
Resume
04
Apply
Local-first · user controlled

Problem

Job-search work is usually scattered across spreadsheets, documents, browser tabs, AI chats, and repeated versions of the same career evidence. The challenge was to create one durable workspace that could manage that growing history while keeping sensitive career data local and AI optional.

Product Choice

CareerTracker organizes the workflow around Company → Role → Resume → Assessment → Cover Letter → Questions/Notes → Status. Career evidence can be reused across applications, documents remain local or user-controlled, and optional AI assists with assessment and writing without automatically overwriting user content.

What It Proved

Consolidated a fragmented job-search process into one local-first workflow centered on companies, roles, documents, evidence, and application status.

Designed AI as optional and bounded, with user review, configurable limits, provider choice, and safeguards against invented career claims.

Took the product through Windows packaging, upgrade testing, migration hardening, production debugging, and public GitHub release.

Refined the interface for long-term use with dense lists, search, filters, dirty-state saves, and workflows designed for hundreds of records rather than demo-scale data.

What I Learned

A product is not finished when the feature works in development. Long-term data growth, upgrades, failures, security, packaging, and installed-user behavior are part of the product experience too.

Project Archive
Multi-Agent PM Assistant

Multi-agent assistant concept for product and delivery workflows.

#Agents#Workflow Design#Product Systems
Tweet Sentiment Analyzer

Built a Hadoop MapReduce pipeline on AWS to perform large scale sentiment analysis of twitter data.

#AWS#JAVA#ETL
Skills, stack, and working system
Skills & Tech Wall

Skills, systems, and tools in my stack.

This section combines how you think with what you use, so it grows naturally as your work evolves.

Technical Product Management
Product Strategy
Roadmapping
Backlog Management
Stakeholder Alignment
Agile / Scrum
MVP & Iterative Delivery
Release Planning
APIs (REST)
System Design Awareness
Distributed Systems
Microservices
Data Modeling
SQL (MS SQL, PostgreSQL, MySQL)
Postman
Swagger UI
RAG
Prompt Engineering
Agentic Workflows
Context Engineering
Token Optimization
AI-Assisted Development
Python
C#
Java
FastAPI
Apache Storm
Azure Service Fabric
Visaul Studio
Post-Trade Systems
Trading Systems
FIX Protocol
Prime Services
Client Reporting Systems
Workflow Automation
Agile / Scrum
JIRA
Confluence
MS Project
Visio
Git
Railwayt
Vercel
Cross-Functional Leadership
Technical Product Management
Product Strategy
Roadmapping
Backlog Management
Stakeholder Alignment
Agile / Scrum
MVP & Iterative Delivery
Release Planning
APIs (REST)
System Design Awareness
Distributed Systems
Microservices
Data Modeling
SQL (MS SQL, PostgreSQL, MySQL)
Postman
Swagger UI
RAG
Prompt Engineering
Agentic Workflows
Context Engineering
Token Optimization
AI-Assisted Development
Python
C#
Java
FastAPI
Apache Storm
Azure Service Fabric
Visaul Studio
Post-Trade Systems
Trading Systems
FIX Protocol
Prime Services
Client Reporting Systems
Workflow Automation
Agile / Scrum
JIRA
Confluence
MS Project
Visio
Git
Railwayt
Vercel
Cross-Functional Leadership
Product
Technical Product Management
Product Strategy
Roadmapping
Backlog Management
Stakeholder Alignment
Agile / Scrum
MVP & Iterative Delivery
Release Planning
Technical Fluency
APIs (REST)
System Design Awareness
Distributed Systems
Microservices
Data Modeling
SQL (MS SQL, PostgreSQL, MySQL)
Postman
Swagger UI
AI in Products
RAG
Prompt Engineering
Agentic Workflows
Context Engineering
Token Optimization
AI-Assisted Development
Engineering Context
Python
C#
Java
FastAPI
Apache Storm
Azure Service Fabric
Visaul Studio
Domain & Platform
Post-Trade Systems
Trading Systems
FIX Protocol
Prime Services
Client Reporting Systems
Workflow Automation
Delivery & Collaboration
Agile / Scrum
JIRA
Confluence
MS Project
Visio
Git
Railwayt
Vercel
Cross-Functional Leadership
Continuous learning and public proof

Open for the next build

Contact

See Something, Say Something.

Explore my work, or reach out to connect...

GitHub

Code, experiments, and public repositories.

Explore
→
LinkedIn

Professional profile and career history.

Explore
→
Substack

Writing, notes, and thought pieces.

Explore
→
LeetCode

Problem-solving and coding practice.

Explore
→
Email

Direct contact.

Explore
→

Let’s turn ideas into product.

Open to conversations around product, systems, and AI-driven applications.