About the job
AWS Identity Analytics is re-imagining how identity data is understood, acted on, and used to protect customers at scale. We build an AI-driven analytics platform that turns 50+ PB of raw logs and metrics into proactive, actionable insights for AWS Identity leadership and core service teams — including IAM and STS. AWS teams across the organization also rely on our platform for impact analysis related to AWS Auth.
Responsibilities
Design, develop, and deploy end-to-end ML solutions — including anomaly detection, time-series forecasting, classification, and optimization models — that turn Identity logs, policies, and metrics into proactive, actionable insights.
Build and operate LLM-powered agents that serve as intelligent interfaces to Identity data, enabling service teams to query, explore, and act on insights conversationally.
Engineer features from petabyte-scale datasets using AWS services (Glue, Athena, EMR) and deploy models to production environments (SageMaker, ECS, EKS).
Partner with AWS Identity leadership, Product managers, IAM, STS, and other service teams to define success metrics, design experiments, validate models, and translate findings into decisions.
Stay at the forefront of innovation by applying state-of-the-art techniques in ML, deep learning, and GenAI to Identity Analytics challenges, fostering rapid experimentation and continuous learning.
Communicate complex technical concepts clearly to audiences of varying technical sophistication, including senior leadership.
Mentor junior engineers and contribute to the team's long-term technical direction.
Qualifications
Minimum
5+ years of non-internship professional software development experience
5+ years of programming with at least one software programming language experience
5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience as a mentor, tech lead or leading an engineering team
Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
Preferred
5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent