Building products at the intersection of AI, technology & real user problems.
Product Management · AI Product Management · Technical Product Management
I lead products from 0→1 through structured discovery, rigorous technical trade-offs, and high-velocity execution. Specializing in generative AI systems, LLM evaluation, and agentic workflows.
Give me a company, role, or product problem. I’ll turn it into a concise product opportunity analysis.
Signature Product Thinking
The 8-Stage 0→1 Product Lifecycle
Deconstruct ambiguity into clear, user-centric problem statements.
How I Think About Products
Product management is not about managing feature requests—it is about discovering real problems, making disciplined technical trade-offs, and driving measurable outcomes.
Start with the Problem
Deconstruct friction and uncover underlying root causes.
Understand the User
Empathy backed by observational data & qualitative feedback.
Validate Assumptions
Test high-risk hypotheses before committing resources.
Define Opportunity Space
Evaluate addressable market value & strategic alignment.
Establish Product Strategy
Align vision, positioning, and technical capabilities.
Prioritize Ruthlessly
Focus on high-impact initiatives using RICE & Kano frameworks.
Build Smallest Valuable Solution
Ship lean, functional MVPs with tight feedback loops.
Measure Outcomes
Track funnel conversion, retention, latency & model performance.
Learn & Iterate
Continuous feedback loop driving product perfection.
Start with the Problem
"Deconstruct friction and uncover underlying root causes."
Never jump straight to solutions or feature requests. Map user pain points, quantify impact, and articulate the exact core problem before writing a line of spec or code.
Product & Engineering Journey
Hands-on professional experience spanning machine learning pipelines, data preprocessing, requirements engineering, and cross-functional product delivery.
Machine Learning Intern
- Performed rigorous Exploratory Data Analysis (EDA), data cleaning, and preprocessing across complex tabular datasets.
- Executed feature engineering pipelines to improve predictive model performance and reduce feature redundancy.
- Developed and benchmarked multiple ML classification and regression algorithms, optimizing hyperparameters via GridSearch and RandomSearch.
- Evaluated model performance using precision, recall, F1-score, ROC-AUC, and mean squared error to align technical metrics with business targets.
Business Analyst Intern
- Gathered business requirements from stakeholders, converting fuzzy user needs into structured technical product requirements.
- Conducted business process analysis and workflow mapping to identify operational bottlenecks.
- Drafted comprehensive Functional Specification Documents (FSD) and Product Requirement Documents (PRDs).
- Facilitated cross-functional stakeholder discussions, feature backlog grooming, UAT testing, and continuous feedback collection.
Published Products on Product Hunt
Live, functional applications published on Product Hunt and built from 0→1 to solve acute user pain points with intuitive interfaces and technical discipline.
Transform raw UI screenshots into structured, actionable Product Requirement Documents (PRDs).
Product Managers spend hours manually writing detailed PRDs from visual wireframes and high-fidelity mockups.
Product Managers, Technical Leads, UX Designers, and Startup Founders.
Zero-friction input (image drop) delivering immediate high-value documentation output.
Multimodal LLM vision pipeline analyzing visual spatial layout, buttons, inputs, and state hierarchy.
Instantly calculate total playlist watch times across variable playback speed multipliers (1.25x, 1.5x, 2x).
Online learners and students struggle to plan study schedules when taking long multi-video courses on YouTube.
Self-directed learners, students, software engineers studying tutorials, and course takers.
Solves a pinpoint utility problem in under 3 seconds with zero signup required.
Algorithmic duration parsing & client-side time unit conversion.
Portfolio Projects
End-to-end product strategy specifications detailing discovery research, user pain points, MVP architecture, AI systems, live code repos, and metric hypotheses.
AI Customer Support Agent
Self-learning autonomous agent capable of resolving 70%+ of tier-1 support tickets using grounded corporate knowledge bases.
SaaS companies face surging support costs and delayed resolution times (8+ hours avg) for tier-1 user inquiries during peak hours.
Customer Success Leads, SaaS Operations Teams, and end-consumers seeking instant answers.
High customer churn driven by delayed responses to repetitive setup and billing queries.
Automate tier-1 support using an AI agent with tool execution (refunds, password resets) without hallucinating.
User research revealed 68% of support tickets involved 5 standard knowledge base categories with predictable resolutions.
Build a human-in-the-loop autonomous agent system that handles standard queries independently and escalates complex edge cases smoothly.
Multi-agent RAG workflow with vector search over docs, strict system boundaries, tool execution, and automated ticket escalation.
User asks query → Agent retrieves relevant context → Formulates solution → Executes tool action if required → Asks user for satisfaction confirmation.
Widget supporting 10 core API integration guides and automated password resets with a 2-click human handover button.
LangChain/LangGraph agent with tools (`searchKnowledgeBase()`, `triggerPasswordReset()`, `escalateToHuman()`).
Vector retrieval with pgvector, strict token context windows, guardrails against prompt injection, latency < 1.2s.
AI as a Product System
AI is not a single feature—it operates across three distinct strategic layers: as the primary product, as an embedded workflow capability, or as an autonomous agent system.
AI as Product
The artificial intelligence model IS the core user value proposition.
Products where generative capabilities or intelligence models form the primary interaction loop. Success relies on prompt interface design, output latency, and accuracy.
Generates structured Product Requirement Documents directly from raw uploaded screenshots using multimodal vision LLM pipelines.
AI as Capability
AI embeds directly into existing software workflows to remove manual friction.
Enhancing legacy user flows without replacing the core UX. Examples include semantic auto-complete, automated tagging, sentiment parsing, and contextual summarize buttons.
Replaces rigid keyword-matching in candidate databases with semantic vector similarity scoring over parsed resume PDFs.
AI as System
Autonomous agents interacting with external APIs, DBs, and tools.
Agentic architectures combining planning loops, memory vectors, tool allowlists, human-in-the-loop fallback gates, and multi-step execution graphs.
Retrieves knowledge base docs, parses customer intent, executes password reset APIs, and escalates edge cases to human leads seamlessly.
Technical Product Systems
Great product managers don't just write feature specs—they understand technical constraints, evaluate architecture trade-offs, and make informed technical decisions.
AI / LLM Architecture
Provide sub-second structured output for automated PRD creation without LLM hallucination.
High token generation latency and unpredictable free-form natural language schema output.
Using open-ended prompts vs strict Pydantic JSON schema output parsing with structured fallback function calls.
Enforce JSON schema validation via Zod + LangChain structured output with automatic retry mechanisms.
Cloud Infrastructure
Support zero-downtime global edge deployments with low cold-start times.
Heavy serverless cold starts when invoking large ORMs or server-side ML packages.
Monolithic VM instances vs Next.js Serverless Edge Functions with lightweight HTTP clients.
Deploy Next.js App Router on Vercel Edge with Supabase REST client and revalidated static caching.
Data & Security
Maintain private single-owner CMS security while allowing public performant portfolio reads.
Prevent service-role key leaks and malicious admin API mutations.
Client-side authentication state vs HTTP-only secure cookie session validation with Postgres RLS.
Strict Supabase RLS policies (public SELECT on published state) + HTTP-only JWT cookie validation.
Observability & Analytics
Track user engagement and API rate-limit breaches without invading visitor privacy.
Third-party tracking script bloat impacting Core Web Vitals (LCP, INP).
Heavy Google Analytics scripts vs lightweight server-side telemetry logging.
Server-side audit logging for admin actions + server-level rate limit counters.
Product Management Toolkit
Core methodologies, analytical tools, and strategic frameworks leveraged to discover, define, build, and scale products.
Product Strategy & Discovery
- Product Strategy
- Product Discovery
- User Research
- Competitive Analysis
- Opportunity Assessment
- 0→1 Product Development
Execution & Specification
- PRD Writing
- Roadmapping
- Prioritization (RICE/Kano)
- User Journeys
- UX Thinking
- Functional Specs
Analytics & Growth
- Product Analytics
- KPI & Metric Definition
- GTM Strategy
- Funnel Optimization
- A/B Testing Hypothesis
- UAT & Feedback Loops
AI & Technical Competencies
- AI Product Thinking
- LLM & RAG Systems
- Agentic Workflows
- API Design & Specs
- Cloud & System Basics
- Technical Tradeoff Evaluation
Certifications & Specializations
Dynamic credentials verified by industry leaders in AI Product Management, Business Analysis, and Cloud Architecture.
AI Product Management Specialization
Comprehensive specialization covering AI product discovery, machine learning problem formulation, model metrics, and ethical AI deployment.
Business Analysis Foundations
Mastery of requirements elicitations, stakeholder interviews, process modeling, state transitions, and acceptance criteria writing.
AWS Certified Cloud Practitioner
Foundational cloud computing architecture, core AWS services (EC2, S3, RDS, Lambda), security standards, and cost optimization principles.
Published Articles & Essays
Deep-dive technical articles on LLM token cost optimization, structured output engineering, cloud infrastructure, and DevSecOps pipelines.
Token Cost Optimization: The Complete Guide to Building Cost-Efficient LLM Applications
Part 1 : Understanding Token Economics, Hidden Costs, and the Fundamentals Every AI Engineer...
Structured Output in LangChain
When I started building LLM applications, one thing became obvious very quickly: Getting a response...
🏗️ Building a Scalable Two-Tier AWS Infrastructure with Terraform
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🚀 DevSecOps Netflix Clone CI/CD Pipeline with Monitoring (Jenkins, Docker, Kubernetes, Prometheus, Grafana)
In this blog, I’m not just deploying a Netflix clone — I’m walking you through a real-world DevSecOps...
Deploying a 2048 Game on Kubernetes using Amazon EKS — End-to-End DevOps Project
Kubernetes has become the de-facto standard for container orchestration, and many organizations today...
Designing a Production-Grade CI/CD Pipeline for Modern Systems
There’s a big difference between: “We have CI/CD” and “Our production pipeline is reliable.” Most...
Designing a Highly Available Web Application on AWS (Production-Grade Guide)
High availability (HA) is not a checkbox — it’s a design philosophy. Most tutorials show you how to...
AWS IAM Explained for DevOps Engineers
If you’ve worked with AWS in a DevOps role, you’ve definitely interacted with IAM — even if you...
How to Build Your First Machine Learning Project from Scratch
Building your first machine learning project can feel confusing at the start. You might know Python,...
Why Most AI Systems Fail in Production..🤔🤯🤖
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AI vs Machine Learning vs Data Science in 2026 – Real Differences with Career Paths
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🐍 Python Programming: From Basics to Control Flow
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📘 The Ultimate Guide to Machine Learning Algorithms
Machine Learning is no longer just a buzzword—it’s shaping industries, automating decisions, and even...
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Understanding MCP (Model-Context Protocol) Abhishek...
Understanding MCP (Model-Context Protocol)
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Adversarial Attacks on Generative AI: A Growing Concern in the AI Era
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📊 Time Series Analysis: A Practical Guide for Data Scientists
“Time is what we want most, but what we use worst.” — William Penn In the world of data, time is...
K-Means Clustering: Understand the Magic Behind Unsupervised Learning
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🤖 Agentic AI: Why Everyone’s Talking About the Future of Autonomous Intelligence
From AutoGPT to LangChain Agents, here’s why Agentic AI is shaping the future of how machines think,...
Neural Networks : A Beginner-Friendly Guide to the Brains Behind AI
Introduction: Why Neural Networks Matter Have you ever wondered how Netflix recommends...
Understanding Feature Engineering: The Hidden Power Behind Data Science Success
🧠 What is Feature Engineering in Data Science? Feature engineering is the process of...
Retrieval-Augmented Generation (RAG): The Future of AI-Powered Knowledge Retrieval
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The Best Data Science Tools for 2025
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The Difference Between AI Agents and Traditional AI Models
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A Beginner's Guide to Helmet.js: Protect Your Node.js Apps
Introduction Web security is essential for any online application. If you're building a...
The Beauty of Clean Code: Why Simplicity Matters
In the ever-evolving world of software engineering, clean code is often hailed as a cornerstone of...
Uploading Images Using Cloudinary in Node.js
Uploading and managing images in web applications is a common requirement, and Cloudinary is one of...
How DevOps Fits with SDLC: Bridging the Gap Between Development and Operations
The Software Development Life Cycle (SDLC) is a structured approach to software creation that...
Decoding JavaScript Emoji Sorting with the Fitzpatrick Scale
When we think of sorting arrays in JavaScript, we usually imagine strings, numbers, or even objects...
Low-Level Design of a Music Player Application
Designing a music player application requires careful planning and structuring of components to...
Featured Repositories & Tooling
Active engagement with open-source product management suites and autonomous agent frameworks shaping developer productivity.
Open-source software development tool to manage issues, sprints, and product roadmaps with modern UX.
AI-driven software development agent capable of writing code, running commands, and fixing bugs autonomously.
Portfolio Intelligence Agent
Provide your hiring objective or role requirements to generate an instant, evidence-backed profile alignment synthesis.
Let's Build Together
Interested in product management roles, technical consulting, or 0→1 AI product strategy discussions? Drop a message below.