Anupreet Walia

AI sales intelligence platform. Knowledge graphs + multi-agent LLMs help sales teams prep for calls, get real-time guidance, and learn from every conversation.

• Set product vision and strategy for a new category of sales intelligence built on structured knowledge, not call transcription. Positioned Brevian against incumbents like Gong and Chorus.
• Owned product roadmap end-to-end. Shipped four products: Meeting Prep, Live Assist, Sales Coaching, and CRM Updates, shaped by customer discovery, competitive analysis, and direct market feedback.
• Built Brevian's Knowledge Engine from scratch: a structured graph connecting products, features, pain points, stakeholders, objections, and deal context, enabling multi-hop reasoning that outperforms standard RAG.
• Designed the multi-agent LLM architecture: three agent types (Chat, Meeting Intelligence, Intelligence), 10+ orchestrated tools, recursive tool calling, and context management across OpenSearch, PostgreSQL, and GPT-4o/5.1.
• Built the GTM-ready product surface: enterprise integrations (Gong, Salesforce, Google Drive, Notion), onboarding flows, and a product-led growth experiment (Pipeline Health Workspace).
• Stood up engineering culture, principles, hiring processes, and a leveling ladder (L3–L8) drawn from Stripe and Netflix.

ML infrastructure startup. Platform for deploying and serving AI models at scale.

• Led cross-functional teams shipping developer experience and model observability features on TRT-LLM and vLLM with a Python backend on Kubernetes.
• Drove SOC2 certification, launched a self-hosted enterprise solution, and tightened product operations across the org.

Cloud analytics platform built on Apache Superset. Enterprise data exploration and visualization. Raised $52.5M.

• Launched three products (SaaS, Hybrid Cloud, and Embedded Platform) with focus on infrastructure performance, cost efficiency, and enterprise compliance.
• Scaled engineering from 20 to 60+ engineers across frontend, platform, data engineering, and infrastructure functions without slowing delivery.
• Owned product and engineering strategy; shipped features while keeping infrastructure scalable, SOC2-compliant, and secure across multi-tenant cloud environments.

Digital mortgage closing platform. Series D, raised $47M+ from Sequoia, F-Prime, and Y Combinator.

• Grew engineering from 11 to 100 engineers. Built frontend, platform, data engineering, data science, and infrastructure teams from scratch. Cut mean time to recovery and reduced regressions.
• Ran QA, DevOps, Data Science, Engineering, and Core Platform, keeping systems stable and scalable through rapid growth.
• Built standardized hiring frameworks, technical interview processes, and evaluation rubrics across the org.

Personal genomics platform. Series B, raised $320M.

• Started the mobile engineering team and shipped Helix's first native iOS app; built a distributed cross-functional team.
• Led the platform team building an iOS SDK, marketplace APIs (React, Go, Swift), and an OAuth2 auth framework so partners could build products on top of genomic data.

• Led a team building Office prototypes with machine learning (Swift, C/C++, Python, Azure ML). Worked with Microsoft Research on an iOS app using MS Band biometric data for stress detection.
• Selected for Microsoft's High Potential (HiPo) program, Microsoft Bench (2015).

• Built full-stack applications, APIs, and data pipelines for Typekit (RoR, Sinatra, JavaScript, Redis, MySQL, Hadoop, Hive).
• Optimized Flash Player and AIR runtime for Android, focusing on battery, memory, and rendering performance.