We are looking for a hands-on leader to build DriveNets’ advanced support organization for the AI Factory. Your organization sits between the customer, our own R&D and AMD and its partners.
Responsibilities
Hire and lead T3 engineers (compute/GPU, fabric, software stack), and define the profiles, specializations and on-call model.
Design the support operating model end to end: severity definitions, SLAs, escalation paths from T2 to T3 to T4 (DriveNets R&D, AMD, hardware partners), and handoffs with case managers and TAMs.
In year one, lead a team that covers T2-level work as well as Helios knowledge is being built, and plan the move to a mature model where most issues are resolved at the first line through ACO.
Be the escalation owner for critical and multi-vendor incidents: run war rooms, coordinate AMD and partner engineering, and own customer communication until resolution.
Run post-incident reviews and make sure fixes reach product, firmware, service packs and the Diagnostics library.
The Presales Solution Engineer – Edge AI is a customer-facing technical leader responsible for transforming Edge AI opportunities into measurable business outcomes and production-ready solutions.
Compensation
The on-target earnings (OTE) range for this role is $148,950.00 - $219,700.00 USD annually with a 80%/20% (salary/incentive) mix. There are additional opportunities for pay in the form of bonuses and/or equity (applies to United States of America candidates only).
Responsibilities
Transform Edge AI opportunities into measurable business outcomes
Lead technical execution of customer POCs, pilots, and solution validations
Design end-to-end Edge AI solutions incorporating appropriate technologies
Develop solution architectures that account for performance, scalability, manageability, security, reliability, cost, and operational requirements
Own technical ownership of moving successful POCs into production deployments
Own end-to-end responsibility for designing optimized compute infrastructure for large-scale AI and machine learning systems, including large-scale distributed training environments.
Responsibilities
Own the end-to-end architecture and design of optimized compute infrastructure for large-scale AI/ML systems, including large-scale distributed training environments, from concept through delivery.
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, and model serving to make rational, well-justified decisions tailored to each client’s situation and standards.
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks, and optimization opportunities, and recommending remediation.
Drive architectural decision-making, documenting rationale, trade-offs, and assumptions so decisions are transparent, defensible, and aligned with business SLAs and standards.
Define and maintain the AI infrastructure roadmap, planning capacity, scaling, and technology evolution in step with business and product goals.
Requirements
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field
Principal AI Infrastructure Architect responsible for designing and deploying large‑scale AI/ML infrastructure, leading technical vision, architecture decisions, and client advisory across hyperscaler cloud platforms.
Responsibilities
Set overarching technical vision and strategy for compute infrastructure supporting large‑scale AI/ML systems
Own complex architecture decisions across compute, networking, storage, orchestration, and model serving
Prototype large‑scale, cost‑optimized compute and distributed training systems
Define reference architectures, standards, and patterns
Lead enterprise‑scale architecture assessments and design reviews
Consultant in EY’s Transformation Execution Practice supporting financial services clients across Wealth & Asset Management, Banking and Capital Markets, and Insurance sectors.
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