Saurabh Tiwari

About Saurabh Tiwari

Technology and AI executive with 20+ years building and scaling enterprise platforms, global engineering organisations, and AI-native operating models across India, APAC, EMEA, and the U.S. I have led 200+ person engineering organisations, architected platforms serving millions of users, and owned multi-year transformations that materially improved delivery velocity, automation, and business outcomes.My work focuses on converting AI strategy and platform architecture into durable, repeatable operating advantage. I specialise in moving organisations beyond AI pilots by building orchestration layers, execution systems, and leadership models that allow AI to scale safely and effectively.A significant part of my career has been spent in high-trust, mission-critical environments, including enterprise platforms for defence and public-sector stakeholders such as the Indian Air Force and the U.S. military, where reliability, security, and governance are non-negotiable. Across these contexts, I have driven enterprise-wide automation, removed systemic bottlenecks, and helped commercialise AI initiatives generating $40M+ in measurable impact.My expertise spans agentic AI systems, enterprise automation, distributed cloud platforms, and operating model design. I operate as a strategic partner to CEOs and executive teams on platform bets, enterprise risk, and technology investment prioritisation, bringing a systems-thinking mindset with pragmatic execution and engineering cultures rooted in clarity, ownership, and sustained performance.I write and speak on enterprise AI scale failures, operating models, and why organisations must redesign systems—not just deploy models—to realise long-term impact.

Experience

1. Director Head Ai Platform & Intelligent Systems Meta Wearables Ecosystem

   Tech Mahindra

   Jul 2024 — Present · Pune, IN, US

   Leading AI-native execution systems within Meta’s wearable ecosystem (Ray-Ban Meta / AR glasses), defining how agentic AI operates across engineering workflows, platform infrastructure, and production environments at scale.• Architected AI-driven control layer governing execution across engineering workflows, integrating LLM-based agents into end-to-end systems spanning validation, triage, remediation, and reporting.• Transformed engineering execution workflows through agentic validation systems, reducing manual effort by ~60% and improving release velocity at scale.• Established agent-driven triage and self-healing systems that classify, route, and resolve issues across severity levels, reducing manual triaging effort by ~70%.• Defined human-in-the-loop execution boundaries, ensuring agent-driven decisions remain controlled, auditable, and aligned with production reliability requirements.• Built AI-native observability systems providing real-time visibility into execution flows, system behavior, and failure patterns across production environments.• Addressed low-signal debugging scenarios through adaptive diagnostics and agent-augmented fallback mechanisms within production pipelines.• Leading globally distributed organizations and systems across US, EMEA, and APAC, aligning teams, workflows, and platforms around AI-native execution models.• Driving the evolution of engineering operating models toward AI-mediated execution, aligning workflows, systems, and teams around scalable, agentic processes.

Education

• Indian Institute of Technology, Kanpur

  Vocational Course, Computer Networks

• Mercy Memorial School, UP

  Intermediate

• Dr. A.P.J. Abdul Kalam Technical University

  MCA, Computers

  2001 — 2004