Rahul Reddy Puchakayala
AI Product Manager — Portfolio

Rahul Reddy
Puchakayala

AI Product Manager

Former Data Engineer at Accenture and Product Manager at Optum — now focused on building AI-powered products. I bring engineering rigor to product decisions: grounding roadmaps in data, shipping with clear success criteria, and deeply understanding the users I build for.

5Portfolio Projects
4+Years Experience
5K+Users Impacted
3Industries
Experience
UNIVERSITY OF TEXAS AT DALLAS
Student Worker — Project Management
Present
Leading end-to-end delivery of academic and technical projects. Implemented Jira & Trello, improving team productivity by 15% and reducing missed deadlines.
OPTUM
Product Manager
August 2022 – May 2024
Owned product roadmaps and PRDs for AI-driven features (Scale Test Co-Pilot). Built metrics dashboards tracking 10+ KPIs, improving performance insights by 25%. Impacted ~5,000+ internal users.
ACCENTURE
Data Engineer
January 2020 – July 2022
Drove 15% revenue increase through data analysis using Hive, Flume & Kafka. Reduced integration time by 30% and improved Hadoop system stability by 20%.

AI/ML PM
Projects

Five projects covering the full PM toolkit — from zero-to-one product strategy to user research, open source sprints, and AI quality metrics.

01
■ Product Strategy

MeetingMind AI — Product Requirements Document

A full PRD for a zero-to-one AI SaaS that transforms meeting recordings into structured action items. Covers user personas, RICE prioritization, go-to-market strategy, pricing, and risk analysis.

Download .docx
Document contains
Problem statement & product vision
2 detailed user personas
User stories & acceptance criteria
RICE prioritization table (6 features)
Success metrics dashboard spec
GTM strategy & pricing model
Risk register with mitigations
02
■ Product Teardown

Notion AI — Deep Dive & Redesign Proposals

A PM-led competitive teardown of Notion AI — analyzing its business model, identifying 3 structural UX gaps through user research, and proposing a prioritized roadmap with concrete success metrics.

Download .docx
Document contains
Business model analysis
3 user research findings
4-product competitive matrix
3 prioritized feature proposals
Quarterly roadmap recommendation
Metrics I would own as PM
03
■ PM Sprint

LocalAI — 90-Day Open Source PM Sprint

A simulation of operating as PM on LocalAI, a 22K-star open source project. Includes user segmentation, north star metric, RICE-prioritized roadmap, 3 full feature specs, and GitHub issues written as PM.

Download .docx
Document contains
4-segment user analysis
North star metric + 5 KPIs
7-feature RICE roadmap
3 full feature specifications
3 GitHub issues written as PM
Day-90 measurement plan
04
■ User Research

AI Trust Study — How Knowledge Workers Trust AI

End-to-end qualitative research study: 10 interviews across 4 industries exploring when and why professionals trust or reject AI suggestions. Includes 5 findings, affinity map, and 3 HMW opportunities.

Download .docx
Document contains
Research plan & screener criteria
Interview guide (5 core questions)
10-participant overview table
5 key findings with evidence
Affinity map summary
3 HMW opportunities + priority matrix
05
■ AI Metrics

Measuring AI — A PM's Metrics Framework

A framework for measuring AI product success — covering the three layers of AI metrics, leading vs. lagging indicators, guardrail metrics with alert thresholds, model drift detection, and a full dashboard spec.

Download .docx
Document contains
3-layer AI metrics model
North star metric definition
5 leading indicators with targets
5 lagging indicators
4 guardrail metrics + alert thresholds
Model drift detection pipeline
Dashboard spec (4 views)