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AT&T brand, remote option, and metro locations increase applicant density despite senior, niche skill requirements.
Deep IAM and cybersecurity plus AI expertise limits cross-industry transferability.
Explicit 12+ years and mandatory ML plus IAM security expertise impose stringent filters.
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Design and develop AI/ML models and solutions to enhance Identity and Access Management functions such as access risk scoring, anomaly detection, and access recommendations.
Lead and guide teams in delivering AI-enabled IAM capabilities, collaborating with cybersecurity, IAM architects, platform engineers, and stakeholders for scalable and secure implementation.
Monitor AI model performance, ensure alignment with security standards, and support explainability, auditability, and governance of AI-driven access decisions.
12+ years of experience in security engineering involving AI/ML/Data Science capabilities.
Hands-on expertise with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost, LangChain) and data platforms (SQL, Spark, Databricks, Snowflake, BigQuery).
Familiarity with cloud platforms like Azure, AWS, or Google Cloud.
Experience with cybersecurity principles, access control models, and enterprise risk management.
Deep experience specifically applying AI/ML solutions within the Identity and Access Management domain, including lifecycle management and access governance.
Proven ability to lead cybersecurity AI initiatives, collaborating cross-functionally with architects, engineers, and data scientists in large enterprise environments.
Strong analytical and problem-solving skills with demonstrated capacity to translate security business requirements into technical AI solutions.