Cyber AI Engineer Manager - Frontline AI Security

EY; Palo Alto (CA); On-site; USD 181,000 - 302,000; Full time

Job summary

EY is seeking a Cyber AI Engineer at the Manager level to lead frontier AI delivery for cybersecurity across client engagements. You’ll blend hands-on software engineering with delivery leadership, building AI workflows from prototype to production and guiding teams through architecture reviews and deployments.

You will mentor consultants, manage timelines and budgets, collaborate with frontier model providers, and translate deployment findings into scalable engineering requirements while

Qualifications

• Bachelor's degree with 5+ years of relevant experience; or graduate degree with 4+ years; hands-on software engineering and AI/ML deployment experience.
• Proficiency in Python, APIs, Git, automated testing, and production delivery; experience deploying frontier AI models for cybersecurity.

Responsibilities

• Lead delivery of AI/ML cybersecurity projects for clients, from requirements through deployment.
• Mentor Senior Consultants and Consultants; own workstreams, timelines, and budgets.
• Collaborate with frontier model providers; translate deployment findings into engineering requirements.
• Support proposals, demonstrations, and account growth with reusable components and playbooks.

Skills

Frontier AI models

LLMs

RAG

Software engineering

Delivery leadership

Tool integrations

Threat detection

Education

Bachelor's degree in Cybersecurity, Computer Science, Information Systems, Engineering, or related field

Graduate degree with 4+ years

Tools

Python

APIs

Git

CI/CD

Containers / IaC

Cloud (AWS/Azure/GCP)

Job description

EY is seeking a Cyber AI Engineer at the Manager level to lead frontier AI delivery for cybersecurity across client engagements. You’ll blend hands-on software engineering with delivery leadership, building AI workflows from prototype to production and guiding teams through architecture reviews and deployments.

You will mentor consultants, manage timelines and budgets, collaborate with frontier model providers, and translate deployment findings into scalable engineering requirements while