Hi, I'm Ajay Sharma —

I optimize systems
that grow products.

Led AI model evaluation at Turing for Fortune 500 clients. 2.8 years across AI and product operations. HelloPM | PM Cohort, AI Specialization.

See My Work → Download Resume
Open to Product Roles ✦
Discovery Problem Space · User Research · Opportunity Delivery Build · Iterate · Ship Distribution GTM · Growth · Feedback Loop PRODUCT MANAGEMENT PIPELINE
0
Years · Product & Ops
0
LLM Interactions Evaluated
0
Data Accuracy Maintained
0
Conversion Uplift · Preplaced
WHAT I'VE BUILT

Real problems. Deliberate decisions. Here's what shipped.

Every project begins with validated research and ends with something functional.

Case Study Live Prototype

Signal — Returns Discovery Tool

Amazon Returns PMs were spending 7–8 hours a week on manual data discovery before writing a single PRFAQ. Signal replaces that — an AI-native tool that delivers ranked, evidence-backed problem synthesis on demand. Built on a three-tier LLM architecture: RAG query nodes → Master LLM Node → Judge Node for hallucination validation. Hallucination mitigation is not a UX feature. It is baked into the architecture.

✦ $62M–$158M annual opportunity quantified
View Prototype → View PRD →
Signal ★
RAG MASTER JUDGE
Live Project AI Agent

LinkedIn Engagement Agent

LinkedIn engagement requires finding the right posts, reading them, and writing something worth saying. Echo automates the discovery and drafting layer. Enter a keyword, trigger a run. Echo searches LinkedIn for highly relevant posts from the last 24 hours via Tavily, extracts the actual post content, and generates a contextually relevant comment. Results land in an organized, dated Google Sheet — post link, content, and generated comment in one row — with an email summary after every run. Automated posting was deliberately excluded. Platform research confirmed LinkedIn detects and penalizes it. Echo handles the low-value layer. You own the part that builds actual presence.

✦ 90% daily workflow time reduced
View Live Product → View PRD →
AI Agent ✦
Case Study Live Project

Swiggy Instamart Retention Feature

64% of Planner-type users were abandoning Instamart for offline vendors when they forgot items — validated through a 15-person primary research survey before a single feature was designed. Built Smart Cart: a Gemini API-powered contextual suggestion engine targeting decision latency under 5 seconds, supported by an A/B test framework designed for 125,000 users targeting a 10% lift in repeat order frequency.

✦ 10% projected lift in repeat orders
View Live Product → View PRD →
Smart Cart ✦
View All Work →
Experience

Where I've Operated

June 2024 – Present

Team Lead (Promoted from Research Analyst)

Turing

  • Led a team of 5–6 analysts executing RLHF and SFT workflows for Fortune 500 AI clients, maintaining delivery standards across high-volume, time-sensitive annotation projects.
  • Evaluated 5,000+ LLM interaction pairs against model alignment protocols — systematically identifying logic gaps and hallucination patterns that degrade training data quality.
  • Served as the primary feedback channel between delivery leads and the annotation team, synthesizing recurring error patterns into specific, actionable model improvement guidance.
  • Ran daily stand-ups to surface operational blockers and resolve edge cases in real time, maintaining >95% accuracy across all client deliverables.
Apr 2023 – Mar 2024

Associate Product Operations Manager

Preplaced

  • Strategized and launched tiered mentorship offerings (Basic vs. Premium), increasing service adoption rates by 10% (from 21% to 31%) and maximizing revenue per user through segmentation.
  • Spearheaded the "Structured Mentorship" feature rollout to standardize user journeys, driving a 21% uplift in trial-to-paid conversion by clarifying value propositions for mentees.
  • Implemented mentor accountability rules and attendance protocols to mitigate service gaps, reducing unattendance rates by 10% and ensuring consistent platform reliability.
Skills & Tools

The Stack I Think In

Product Skills
User Research A/B Testing Funnel Optimization GTM Hypothesis Validation Retention Modeling User Story Mapping Product Teardowns PRDs Roadmapping
Analytics
SQL Mixpanel Behavioral Analytics Metric Definition Data Visualization Google Analytics Excel / Sheets
AI & Operations
RLHF SFT Annotation LLM Ops Prompt Evaluation Process Design Quality Frameworks
Tools & Platforms
n8n (Automation) Jira Figma Google AI Studio React Supabase Notion Miro Zapier Loom
WHO I AM
Ajay Sharma

The background looks unconventional. The direction is deliberate.

I'm Ajay — a product builder whose background starts inside AI systems, not outside them. 1.8 years running RLHF quality loops for Fortune 500 AI clients taught me exactly where models fail and why. That ground-level knowledge is what I bring to product work — from how I frame a problem to how I design around it to how I define what success looks like after it ships.

Read My Full Story →
FAQs

Frequently Asked

Yes — actively targeting APM and PM roles at AI-first startups and product-led SaaS companies. Open to remote, hybrid, and on-site.

My B.Tech is in Electrical and Computer Engineering — that's where the systems thinking comes from. The more relevant question is whether I understand how AI products fail and how to build around that. The work answers that better than any degree would.

Early-stage startups and product-led SaaS companies where AI is a core product decision — not a feature added later. Teams that run experiments, hold metrics seriously, and want someone who has been inside the feedback loop, not just read about it.

Give me the problem and the metric. I'll find the approach. I work best with clear ownership, direct feedback, and teams that treat every decision as a hypothesis worth testing.

If you're building AI products, let's talk.

Open to APM and PM roles at AI-first startups and product-led SaaS companies.

Email Me → GitHub → Connect on LinkedIn →