• Lead large-scale AI infrastructure deployment programs across multiple sites, regions, or business units. • Drive end-to-end project execution for GPU clusters, AI compute environments, storage platforms, high-speed networks, and data center infrastructure. • Develop integrated project plans, implementation strategies, and operational readiness frameworks. • Manage cross-functional coordination between engineering, operations, supply chain, vendors, and executive stakeholders. • Identify and mitigate program risks, schedule impacts, technical dependencies, and operational constraints. • Lead infrastructure migration, expansion, upgrade, and modernization initiatives. • Drive governance reviews, project reporting, KPI tracking, and executive-level communications. • Coordinate infrastructure acceptance testing, deployment validation, and production readiness activities. • Mentor junior project managers and contribute to PMO process standardization and operational maturity. • Support vendor negotiations, technical evaluations, and infrastructure planning initiatives.

• Assist in creating and updating AutoCAD drawings, rack elevations, cabinet layouts, and structured cabling documentation. • Support data hall design documentation, asset tracking, and revision management activities. • Assist engineering teams with infrastructure inventory validation and basic capacity tracking. • Help maintain design standards, templates, and project documentation repositories. • Participate in engineering reviews, design walkthroughs, and quality assurance activities. • Coordinate with cross-functional teams to gather project inputs and update design records. • Support preparation of project reports, spreadsheets, diagrams, and technical documentation. • Learn data center infrastructure concepts including power, cooling, cabling, rack configurations, and AI cluster environments. • Follow established operational procedures, engineering standards, and safety requirements. • Assist with administrative and project coordination tasks related to infrastructure deployment activities.

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction. • Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact. • Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure. • Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls. • Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis. • Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization. • Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows. • Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments. • Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership. • Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction. • Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact. • Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure. • Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls. • Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis. • Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization. • Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows. • Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments. • Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership. • Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.