Cybersecurity AI_ML Engineer

Why GMF Cybersecurity?

Innovation isn’t just a talking point at GM Financial, it’s how we operate. By joining our team, you’ll work in a mission-focused environment with specialized teams, including Engineering, Threat Intelligence, Vulnerability Management, Incident Response, Firewall, Governance, Risk, Architecture and Offensive Security. These teams collaborate to identify, manage and respond to threats, all while driving innovation across the environment.

Cybersecurity is central to our strategic vision, so you’ll benefit from exceptional leadership visibility, with direct reporting lines to the CEO. This structure ensures your work is recognized and supported at the highest levels, while also enabling bold innovation and the adoption of cutting-edge technologies.

Shape the future of Cybersecurity at GM Financial, with the freedom to explore, the tools to build and the support to thrive. 

This position will be posted until filled.

About the Role: 

The Cybersecurity AI_ML Engineer is responsible for developing, deploying, monitoring, tuning, evaluating, reporting and maintaining systems and procedures; and to identify and mitigate threats to the corporate network, corporate assets and corporate users. This team member will identify core requirements, design and implement security technologies and work with stakeholders to perform ongoing tuning and alerting on those technologies. This role also incorporates advanced AI and machine learning methodologies to transform cybersecurity data into scalable detection capabilities, enhance analytics, and improve threat detection under complex and adversarial conditions. 

In this role you will: 

• Prepares technical requirements and standards
• Assists in the identification, engineering and designing of security technologies including, but not limited to: Security Incident and Event Managers (SIEM) and threat intelligence solutions, Intrusion Detection and Prevention Systems (IDS/IPS), Endpoint security solutions, Web Application Firewalls (WAF), Cloud Security, VPNs and Firewalls
• Performs analysis of system logs to identify unauthorized use or access
• Creates, analyzes and communicates security metrics to leadership
• Participates in emergency response and security incident activities
• Recommends and evaluates security tools to identify more efficient and effective security measures
• Develops and deploys machine learning models for threat detection (anomaly detection, classification) 
• Builds feature engineering pipelines from security telemetry (logs, endpoint, network data) 
• Implements and manages ML model training, experimentation and tuning workflows 
• Deploys ML models using containerized environments (Docker, Kubernetes) 
• Monitors model performance, drift and detection accuracy in production 
• Applies AI-driven insights to threat hunting and incident response 
• Collaborates with engineering and infrastructure teams to support scalable ML-enabled security systems 

What makes you an ideal candidate?

• Strong knowledge of networking concepts, protocols, and infrastructure security
• Advanced knowledge in Infrastructure design and management
• Working knowledge of management processes such as personnel administration, planning and budgeting
• Strong working knowledge of Intel platforms, iSeries and pSeries servers
• Advanced understanding of IT Service Management (ITSM) best practices and processes
• Experience with UML Design Tools
• Advanced knowledge of TCP/IP, OSI model and imp subnetting
• High level understanding of technology infrastructure, security concepts and platforms
• Deep understanding of machine learning techniques including anomaly detection and statistical modeling 
• Experience with unsupervised, semi-supervised and advanced modeling approaches 
• Strong foundation in probability, statistics and data analysis 
• Experience designing experiments, validation strategies and evaluating model performance 
• Expert-level Python for data science and machine learning (e.g., pandas, scikit-learn, PySpark) 
• Experience with large-scale data processing and distributed data systems 
• Ability to translate ambiguous cybersecurity problems into measurable analytical solutions 
• Understanding of adversarial ML concepts and model robustness (preferred) 
• Experience with LLMs, deep learning or AI-assisted detection (preferred)
• Demonstrated success in project management
• Advanced knowledge of the OSI model and security that is associated with each layer
• Understanding of routing an