Cybersecurity AI_ML Engineer

GM Financial•Irving, TX

6d•Hybrid

About The Position

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.

Requirements

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 and switching protocols as they relate to load balancing
Strong understanding of application layer protocols including HTTP, SSH, SSL and DNS
Communicates quickly, clearly, concisely, appropriately and intelligently
Foster open communication, speaks with impact, listens to others and writes effectively
Ability to effectively negotiate with vendors on upgrades and acquisitions
Effective planning, time management, negotiation and delegation skills
Expert level IT security processes and controls knowledge as well as IT infrastructure and networking technical knowledge
Ability to approach problems with an open-mind and create new and innovative ideas and methods
Advanced technical writing
Experience in documentation tools such as Visio and Microsoft Office products
Advanced information security standards/frameworks (i.e., NIST Cybersecurity Framework, ISO 27001) skills
Advanced experience with Network and VLAN segmentation
Strong analytical skills
Ability to approach problems with an open-mind, use existing information and resources
Creative, innovative, problem-solving and maximizing your potential to solve problems and improve methods
Think positively when faced with obstacles, build on others ideas, think logically and intuitively
Detailed oriented
1-5 years of experience in large and complex business environments with a successful track record working directly with senior level management preferred
1-5 years of experience in one or more of the following domains: Cybersecurity, Information Security, Data Science or related field.
1-3 years of experience with developing or supporting machine learning models in a security context preferred.
High School Diploma or equivalent required
Bachelor’s Degree in Cybersecurity, Computer Science, Data Science or related field or equivalent work experience strongly preferred

Nice To Haves

Understanding of adversarial ML concepts and model robustness
Experience with LLMs, deep learning or AI-assisted detection
One or more security related certifications, such as CISSP, CCNP-Security, GIAC, CEH, or CPTS highly preferred
AI/ML certifications are a plus.

Responsibilities

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