Chaitanya Mukkamala
Product Leader · AI/LLM Platforms · Toronto, Canada
I build AI platforms that don't just solve today's problem — they create leverage for the next ten. My focus is finding where a well-placed system changes behavior at scale. Every roadmap I own is a hypothesis about where the market is going, not where it's been.
AI/LLM Product Strategy
Translate LLM capabilities into personalization and automation platforms that drive measurable revenue — not demos.
Platform Architecture at Scale
Design systems that handle 100B+ API calls annually. Lead platforms from 0→1 through global rollout.
Cross-Org Leadership
Align product, engineering, and science teams across complex orgs. Hire selectively, develop intentionally, maintain a high bar.
“Turns ambiguity into clarity while guiding product pivots and aligning cross-organizational teams around focused, customer-centric strategies.”
I believe the most durable products are built at the intersection of deep customer understanding and structural business leverage. I don't optimize for feature count — I optimize for systems that compound.
At Amazon, I own platforms that influence $46B+ in revenue not because we built the most features, but because we built the right primitives: flexible enough to serve 50+ teams, opinionated enough to enforce quality at scale.
“Write the brief before the meeting.”
I document the problem, hypothesis, and key decisions before any alignment meeting. If I can't write it down, I don't understand it yet.
“Prototype in the first week.”
Every roadmap item starts with the smallest possible test. The artifact — however rough — forces clarity that slides never do.
“Own the portfolio, not just the product.”
My roadmaps account for adjacent teams, downstream dependencies, and the five things that would make this fail.
“Hire for judgment, develop for promotion.”
I hire people who can make hard calls without me, then invest in making sure they have what they need to grow.
“Measure what changes behavior.”
Not metrics that look good — the specific signal that tells me whether the product did what the customer actually needed.
Problems I'm thinking about. Not specs — hypotheses.
Agentic QBR generation
An agent that ingests Slack threads, Jira velocity, and dashboards to auto-draft quarterly business reviews. The data exists — the bottleneck is synthesis.
Prompt version control for enterprise AI
A platform-level approach to managing prompt evolution across large orgs. Like Git for AI behavior — with rollback, A/B testing, and attribution.
AI-native advertiser intelligence
Move from 'here are your stats' to 'here's what you should change and why' with predicted impact. The reporting platform exists. The reasoning layer is the gap.