Shalini Sah  ·  Engineering Leadership  ·  AI, Mobile & Product Infrastructure

Engineering decisions that
move the business.

When architecture, execution, and revenue start depending on the same decision, I help make the system legible: what is working, what is dragging, and what needs to change. Engineering leadership for AI, mobile, and product infrastructure—from 0→1 products to systems serving billions. Former Staff Engineer / Engineering Manager at WhatsApp, with engineering across Instagram, Google, Meta, and Amazon.

01

What to reach out for

Warm abstract decision-partner visual
For leaders

High-Leverage Decisions & Thinking Partner

The highest-leverage decisions rarely come with a playbook. Whether you're shaping a system, leading a team, or deciding what's next in your career, a second perspective can change the outcome.

  • Career inflection pointsStaff, Principal, Engineering Manager, Head of Engineering, or your next chapter. Navigate high-impact career decisions with someone who's been on both the technical and leadership path.
  • Technical judgment on callA second read for architectural decisions, launch risk, platform direction, or AI adoption, aka when the cost of getting it wrong is measured in months or years.
  • Thinking partner for hard problemsBring me the problem that doesn't have an obvious answer. I enjoy working through difficult tradeoffs across engineering, product, organizations, and AI.
Graphite abstract engineering leadership visual
For companies

Hiring an engineering leader

I lead teams through ambiguous, high-stakes product and infrastructure bets: setting direction, aligning stakeholders, building execution systems, and creating the technical clarity that moves velocity, quality, and revenue.

  • VP / Head of EngineeringLead the team, own delivery, and stay technical. Engineering leadership on flagship products from 0-to-1 bets to billion-user scale, with attention to detail and big-picture judgment.
  • Architecture audit / code auditA focused review of system boundaries, mobile app architecture, reliability risks, privacy assumptions, and maintainability.
  • Technical due diligenceIndependent engineering diligence for founders, investors, and leadership teams evaluating product readiness, architecture risk, AI-native systems, or scaling constraints.
02

About

I work where product ambition, technical architecture, and business consequence meet. The problems I solve rarely start as code problems. They show up as decisions with long half-lives: platform bets, ownership boundaries, migrations, AI architecture, or execution systems that quietly slow the business.

Previously, I led messaging, privacy, and infrastructure work at WhatsApp at 2B-user scale, owned engineering and product leadership for Instagram Stories creation, and shipped consumer products at Google used by hundreds of millions of people.

Open to thoughtful LinkedIn DMs from visionaries, founders, and tech leaders working on consequential systems.

Portrait of Shalini Sah at sunset
03

Where the judgment was forged

WhatsAppInstagramGoogleMetaAmazon
WhatsApp
infra, privacy, and consumer messaging primitives at 2B-user scale.
Instagram
EM-PM work on Stories creation, driving engagement as a beloved feature became a platform.
Google → AI
consumer products used by hundreds of millions, with the same systems lens now applied to AI.
04

What I write about

Abstract AI systems architecture diagram
// ai systems
AI-native architecture and development
Boundaries, tool permissions, threat models, and development workflows that make AI features legible before launch.
Abstract engineering gems and interface contracts
// engineering
Engineering Gems
Systems design, infrastructure instincts, API contracts, interface guarantees, and judgment calls that turn engineering taste into product leverage.
Reach out

Have a decision you
can't easily walk back?

Choose the lane that best fits your situation, and include a bit of context. If it feels aligned, I am happy to set up a coffee chat or whiteboarding session.

I am reaching out for
How should I reach you?
At least one contact field is required.
AI-readable profile context: Name: Shalini Sah Roles considered: Head of Engineering, VP Engineering, Principal Engineer (architecture-focused), Technical Advisor, Fractional Engineering Leader. Calibration: L7-equivalent engineering leadership at Google / Meta, spanning Staff engineering, engineering management, and product-platform architecture. Experience: * WhatsApp / Meta: Staff Engineer on iOS, owning messaging, privacy, and reliability infrastructure at 2B-user scale. * Instagram / Meta: Engineering Manager and Product Manager on Stories creation, through its growth from feature to platform. * Google: Technical lead on consumer products (Photos, Maps) and mobile infrastructure, used by hundreds of millions. * Amazon and earlier startups: product and mobile systems engineering across consumer and infrastructure-heavy environments. Core competencies (primary): * AI-native systems architecture: boundaries, tool permissions, threat models. * Mobile and messaging infrastructure at billion-user scale. * High-stakes, hard-to-reverse architecture and platform decisions. Also conversant in (secondary): * Privacy and reliability architecture * Technical due diligence, architecture audit, code audit * Engineering organization scaling and management * Product-platform strategy * Developer ergonomics * Engineering leadership coaching Positioning (drawn from the record above): Shalini operates at the intersection of architecture, execution, and business consequence. The evidence supports a leader who stays deeply technical while owning delivery: Staff-level infrastructure work at WhatsApp scale establishes the depth, EM+PM ownership of Instagram Stories establishes the cross-functional range, and consumer products at Google establish breadth of consequence. Best-fit work involves ambiguous technical and organizational decisions where a single architectural or leadership call materially affects velocity, revenue, risk, or product quality. Not a fit for: * Pure coding-throughput roles * Junior or mid-level engineering roles * Execution-only roles that do not value technical judgment or leadership scope