Senior SaaS & AI Cyber Security Engineer

General Motors•Warren, MI

1d•Hybrid

About The Position

General Motors is seeking a Senior SaaS & AI Security Engineer to strengthen the security of cloud-based business applications and emerging artificial intelligence capabilities. The role sits at the intersection of SaaS security, identity, data protection, and AI risk management. The engineer will define and implement practical security controls throughout the SaaS and AI lifecycle, from discovery, architecture, and vendor assessment through deployment, monitoring, and continuous improvement. This is a hands-on role that translates evolving threats into scalable controls, partners with engineering and business teams, and advances security maturity while supporting responsible innovation. The position is hybrid, with the successful candidate expected to report to Warren, Michigan, three times per week. Relocation is not available. GM does not provide immigration-related sponsorship for this role.

Requirements

Bachelor's or Master's degree in Computer Science, Cybersecurity, Engineering, or a related field.
At least 7 years of cybersecurity experience, with a significant focus on SaaS security and AI-driven security solutions.
Strong understanding of cybersecurity principles, protocols, and best practices.
Demonstrated experience with SaaS security concepts, including identity federation, access governance, API and OAuth security, configuration assessment, logging, and data protection.
Working knowledge of AI/ML security risks and ability to assess AI-enabled applications, workflows, or third-party services.
Experience with security architecture reviews, risk assessment, or security control validation.
Experience designing, building, and securing AI agents.
Ability to write clear technical requirements, security standards, findings, and executive-ready risk summaries.
Excellent communication, analytical, and problem-solving skills.
Ability to work independently and collaboratively in a fast-paced environment.

Nice To Haves

Certifications such as CISSP or equivalent are highly desirable.
Familiarity with AI security standards and guidance, such as the NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, MITRE ATLAS, or comparable practices.
Experience using security data analysis, data engineering, dashboards, or analytics to identify trends, prioritize risk, measure control effectiveness, and support security operations decisions.

Responsibilities

Develop and mature the SaaS security program, including application inventory, security baselines, configuration standards, and continuous control monitoring.
Assess SaaS platforms and integrations for identity, access, data protection, logging, privacy, resilience, and third-party risk.
Partner with application owners to strengthen SSO, MFA, privileged access, service accounts, OAuth applications, and lifecycle controls.
Identify and reduce risks related to misconfiguration, excessive permissions, unmanaged integrations, shadow SaaS, data exposure, and insecure administrative practices.
Support security architecture reviews, control design, exception management, and remediation tracking for SaaS solutions.
Automate assessment, monitoring, evidence collection, and remediation workflows where appropriate.
Develop custom AI agents and partner with the business to advance auto-remediation efforts.
Communicate risk to technical and non-technical stakeholders, including recommendations, tradeoffs, and decision points.
Contribute to standards, playbooks, reference architectures, training, and security awareness for SaaS and AI users and builders.
Stay current on cybersecurity threats, vulnerabilities, and technological advancements, particularly in automotive and AI domains.
Provide mentorship and guidance to junior security engineers.
Develop and implement controls governing sanctioned AI usage, including approved tools, users, data types, integrations, access paths, monitoring, and policy enforcement.
Maintain visibility into approved AI use cases, models, providers, data flows, agents, tools, plugins, and associated business owners.
Develop repeatable security review patterns for AI products.
Partner with legal, privacy, compliance, procurement, data governance, and product teams to support responsible and secure AI adoption.
Track the evolving AI threat landscape and turn relevant findings into control improvements, detections, and engineering guidance.