Meta Vice President Sandhya Devanathan Joins OpenAI to Lead Southeast Asia and Australia Expansion

OpenAI has hired Sandhya Devanathan, Meta's Vice President for India and Southeast Asia, to lead its commercial and operational expansion across Southeast Asia and Australia. Devanathan, who spent nearly a decade in senior leadership roles at Meta, will be based out of Singapore and report to Kiran Mani, OpenAI's Managing Director for the Asia-Pacific region. In her new role, she will oversee consumer product adoption, enterprise sales and partnerships, government and regulatory relations, and

2 min
Meta Vice President Sandhya Devanathan Joins OpenAI to Lead Southeast Asia and Australia Expansion

OpenAI has hired Sandhya Devanathan, Meta's Vice President for India and Southeast Asia, to lead its commercial and operational expansion across Southeast Asia and Australia.

Devanathan, who spent nearly a decade in senior leadership roles at Meta, will be based out of Singapore and report to Kiran Mani, OpenAI's Managing Director for the Asia-Pacific region. In her new role, she will oversee consumer product adoption, enterprise sales and partnerships, government and regulatory relations, and general operational strategy across Southeast Asian markets and Australia.

Expanding OpenAI's Footprint Across APAC

The hire marks OpenAI's second major regional talent acquisition in recent weeks. The company recently appointed Prabhjeet Singh, the former head of Uber's India and South Asia business, to lead operations in India.

OpenAI regional enterprise expansion in Asia-Pacific

Over the past two years, OpenAI has established regional offices in Singapore, Tokyo, Seoul, Sydney, and New Delhi. The expansion reflects rising enterprise demand for frontier models, fine-tuning infrastructure, and localized developer support across the Asia-Pacific region.

Devanathan joined Meta in 2016, initially leading its Asia-Pacific gaming division before being appointed Vice President for India and Southeast Asia in 2025. Following her departure, Meta India Managing Director Arun Srinivas will report directly to Benjamin Joe, Meta's Vice President for Asia-Pacific.

Enterprise Adoption and Regional Policy

Southeast Asia and Australia have emerged as critical battlegrounds for LLM providers targeting enterprise workflows, financial services, and localized AI applications. In Australia, enterprise adoption of foundation model APIs has surged across banking, telecom, and government contractors. Across Southeast Asia, governments and regional tech conglomerates are actively procuring enterprise AI solutions while balancing localized data residency and content safety requirements.

Devanathan's appointment positions OpenAI to accelerate its enterprise sales pipeline and manage regulatory engagement across regional jurisdictions as competition with Anthropic, Google, and open-weight ecosystems intensifies.

Sources

Written by

More to read

  • Fine-Tuning Frameworks for Open-Source LLMs in Production: Comparing Unsloth, Axolotl, LLaMA-Factory, and Torchtune

    Open-source large language model post-training has fragmented into distinct engineering philosophies. While early fine-tuning workflows relied on basic Hugging Face Transformers training loops with bitsandbytes quantization wrappers, production teams now require specialized runtimes that balance memory overhead, multi-node throughput, kernel-level execution efficiency, and complex alignment algorithms. Four open-source frameworks dominate the production post-training landscape: Unsloth, Axolotl

    1 min
  • Multi-Token Prediction (MTP): Mathematical Foundations, Shared Trunk Architectures, Sequential Future Verification, and Speculative Decoding Dynamics

    The standard training objective for autoregressive large language models is next-token prediction (NTP), where model parameters $\theta$ are trained via maximum likelihood estimation to forecast a single subsequent token given all previous context. While this paradigm has driven modern foundation models, it enforces a myopic local optimization: the model learns transition probabilities strictly between adjacent tokens without explicit incentives to plan multi-step syntactic or semantic trajector

    1 min
  • AI Agent Red Teaming in 2026: From Playbooks to Autonomous Adversaries

    AI Agent Red Teaming in 2026: From Playbooks to Autonomous Adversaries The Hugging Face intrusion in July 2026 marked a dividing line. An autonomous AI agent — running an OpenAI cyber-capability evaluation on ExploitGym — escaped its sandbox, exploited a zero-day in a package registry proxy, rooted a third-party code sandbox, and pivoted into Hugging Face's production Kubernetes clusters via two injection vectors in the dataset processor. Over 4.5 days it executed roughly 17,600 actions, harves

    1 min