Manager, AI Deployment Engineering

Responsibilities

• Build, manage, and develop a high-performing team of Applied AI Engineers supporting complex enterprise customers.
• Own solution design, implementation, production readiness, adoption, and expansion across the team’s customer portfolio.
• Coach engineers on architecture, model selection, evaluations, reliability, latency, safety, security, governance, and cost.
• Establish account prioritization, coverage, escalation, and delivery operating models.
• Serve as a senior technical escalation point for launches, production incidents, integrations, and high-stakes customer decisions.
• Partner with customer executives and technical leaders to connect implementations to measurable outcomes.
• Collaborate with Sales, Solutions Engineering, Product, Research, Engineering, Security, and Legal.
• Translate customer needs into feedback for internal product and engineering teams.
• Develop reusable architectures, evaluation methods, playbooks, tooling, and technical enablement.
• Measure implementation quality, adoption, customer outcomes, delivery quality, team capacity, and reusable-work impact.
• Hire, coach, and develop team members while fostering accountability, collaboration, inclusion, and continuous learning.

Requirements

• Significant experience managing customer-facing technical teams, such as Applied AI Engineers, Solutions Architects, Forward-Deployed Engineers, Customer Engineers, or Technical Account Managers.
• Experience building or leading teams implementing complex software, data, machine learning, or AI systems in enterprise environments.
• Technical depth sufficient to evaluate architectures, challenge assumptions, and coach engineers through difficult implementation decisions.
• Experience taking AI, machine learning, or other technically complex systems from prototype to production.
• Understanding of reliability, observability, security, privacy, data governance, evaluation, and operational readiness.
• Experience leading teams through ambiguity, competing priorities, escalations, and evolving products or markets.
• Ability to translate among technical details, customer needs, product strategy, and business outcomes.
• Experience with large organizations, multiple business units, senior stakeholders, procurement processes, or governance requirements.
• Strong executive presence and customer-facing judgment.
• Experience designing operating models, coverage strategies, prioritization frameworks, or repeatable delivery processes.
• Strong cross-functional collaboration and resource allocation skills.
• Track record of coaching team members, raising performance, and building inclusive teams.

Nice-to-haves

• Experience leading applied AI engineering, solutions architecture, forward-deployed engineering, professional services, technical consulting, or customer engineering teams.
• Experience implementing generative AI, machine learning, developer platforms, cloud infrastructure, or data platforms.
• Experience supporting global customers, regulated workflows, multi-business-unit implementations, or organizational transformations.
• Experience building technical enablement, reusable solution patterns, evaluation frameworks, or customer implementation methodologies.
• Experience with semiconductor, media, entertainment, or similarly complex technology-intensive industries.