2 дня назад
Director, AI Engineering
250 000 - 420 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Director, AI Engineering (AI): Owning the architecture and technical strategy of production AI systems from design through deployment and optimization with an accent on ML infrastructure, model serving, governance, and reliability. Focus on leading AI engineering teams, scaling cloud and MLOps platforms, optimizing inference, and translating business requirements into resilient technical architecture.
Location: United States, remote
Market compensation: $250,000–$420,000 annually
Company
is a specialist executive search firm focused on leaders who help organizations navigate AI transformation.
What you will do
- Own end-to-end architecture and technical strategy for AI systems from design through deployment and optimization.
- Lead and mentor AI/ML engineers, architects, and platform specialists while setting technical standards and code review practices.
- Partner with product, data, and infrastructure teams to integrate AI capabilities into production applications and establish governance frameworks.
- Make decisions on model selection, infrastructure platforms, and tooling, balancing performance, cost, and scalability.
- Build the AI engineering roadmap and prioritize technical debt, infrastructure improvements, and capability expansions.
- Own observability, testing, and quality assurance practices for production AI systems while communicating technical opportunities and constraints to executives.
Requirements
- 10+ years of software engineering or AI/ML infrastructure experience, including at least 5 years in a leadership or architect role.
- Experience designing and shipping production AI systems at scale, with hands-on fluency in ML frameworks, model deployment, and inference optimization.
- Proven ability to build and lead technical teams, including hiring, coaching, and retaining engineers.
- Deep knowledge of cloud platforms including AWS, GCP, and Azure, as well as containerization.
- Knowledge of MLOps tooling, data pipelines, and model-serving infrastructure.
- Ability to translate business requirements into technical architecture and communicate with technical and non-technical stakeholders.
Culture & Benefits
- Permanent remote work from the United States.
- The posting is for future opportunities and adds applicants to a candidate network for relevant roles.
- Work in fast-moving environments involving ambiguous problems and iterative decision-making.
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