Principal Ai/ml Engineer (cybersecurity & Aiops)

Ford Ford · Auto · Dearborn, MI +1 · Enterprise Technology

Principal AI/ML Engineer focused on cybersecurity and AIOps, leading the design, development, and deployment of AI/ML solutions. This role involves building and operationalizing models for anomaly detection, predictive analytics, and classification, with a strong emphasis on Generative AI and Agentic AI for automation and threat intelligence. The engineer will architect ML pipelines, work hands-on with data, and translate technical outcomes for stakeholders, bridging data science with security operations.

What you'd actually do

  1. Lead the design, development, and deployment of advanced AI/ML solutions focused on cybersecurity, network analytics, and enterprise risk detection.
  2. Build and operationalize machine learning models, including anomaly detection, predictive analytics, and classification systems for security and network use cases.
  3. Drive the implementation of Generative AI and Agentic AI solutions to enhance automation, threat intelligence, and decision-making workflows.
  4. Architect end-to-end ML pipelines, ensuring scalability, reliability, and integration with enterprise systems.
  5. Work hands-on with data—ingestion, preprocessing, feature engineering, and model tuning—to deliver production-grade solutions.

Skills

Required

  • Python
  • SQL
  • machine learning model development
  • evaluation
  • deployment
  • cybersecurity concepts
  • network architectures
  • Identity & Access Management (IAM)

Nice to have

  • Azure
  • AWS
  • GCP
  • MLOps practices
  • Power BI
  • JIRA

What the JD emphasized

  • Proven experience in designing, building, and deploying scalable AI/ML solutions in enterprise environments
  • Hands-on experience in machine learning model development, evaluation, and deployment

Other signals

  • designing intelligent systems
  • detect risks
  • optimize network performance
  • automate threat response
  • Generative AI and Agentic AI solutions
  • end-to-end ML pipelines