Top Tech Jobs & Startup Jobs

Manages APAC datacenter construction and lease project portfolios through governance, executive reporting, risk mitigation, budget and project controls, vendor oversight, compliance, and cross-functional coordination. Tracks cost, schedule, scope, milestones, KPIs, safety, and security metrics while supporting audits, standardization, continuous improvement, and AI-enabled workflow innovations. Partners with regional leaders, project teams, contractors, consultants, authorities, and internal stakeholders to deliver safe, compliant, predictable, high-quality infrastructure projects.

Leads complex AI and business process transformation sales opportunities across assigned customers. Responsibilities include executive discovery, solution envisioning, business value assessments, competitive positioning, proposal development, business-case creation, partner coordination, and transformation roadmap development. The role builds senior stakeholder relationships, advances qualified pipeline revenue, aligns customer success plans and KPIs, and serves as a trusted specialist for Microsoft Business Applications, Copilot, Dynamics 365, Power Platform, and AI solutions.

Lead delivery of large, complex Microsoft Cloud and AI engagements across the project lifecycle: envisioning, planning, execution, consumption, financial management, risk and change control, stakeholder/steering-committee communication, IP reuse, presales support, and project closure. Drive customer satisfaction, profitability, and business growth while mentoring teams and standardizing processes and artifacts.

Manage transition of new datacenters into Operations as the single point of contact across engineering, construction, and commissioning. Plan and oversee transition activities, coordinate stakeholders, support site mobilization, perform acceptance walks, review turnover documentation, engage suppliers, and capture lessons learned to ensure operational readiness and compliance.

Lead threat actor tracking and analysis using AI, build pipelines and tooling to deliver machine-speed intelligence, produce customer- and executive-facing reports, partner with product and research teams to operationalize intelligence across Microsoft Defender, Sentinel, and managed services, and represent Microsoft in briefings and industry forums.

Lead and translate AI research into developer products: build datasets from public and internal code, design and run large-scale ML experiments, train state-of-the-art models, collaborate with product teams across Microsoft and GitHub, and evaluate and iterate AI systems for software engineering.

Leads applied research for enterprise retrieval, ranking, grounding, knowledge representation, evaluation, and autonomous AI workflows. Develops multi-hop retrieval, semantic enrichment, knowledge graphs, agent memory, trust-aware authorization, and knowledge acquisition systems. Partners with engineering and product teams to productionize scientific innovations at hyperscale. The role requires translating machine learning research into enterprise AI systems and influencing platform architecture and cross-team roadmaps.

Leads technical direction, engineering execution, and people development for a team delivering Azure Local capabilities. Drives architecture, implementation, integration, deployment, reliability, security, observability, and operational excellence for large-scale distributed cloud systems. Partners across engineering, product, customers, and field teams to manage dependencies, make technical trade-offs, deliver end-to-end solutions, and develop technical leaders.

Deliver end-to-end cloud and on-premises infrastructure solutions on Azure, design and implement secure, scalable landing zones, automate with IaC and DevOps, integrate AI workloads, perform security and governance assessments, run workshops, and ensure technical quality and risk mitigation for enterprise clients.

Lead technical ownership of InfiniBand and GPU interconnect fabric operations for hyperscale AI supercomputers. Drive incident response, deep multi-layer debugging across hardware/firmware/drivers/OS, and define failure models, playbooks, telemetry, and automation to improve reliability, observability, and scalability of GPU clusters and platform SLAs.