Senior Machine Learning Engineer, Aws Identity Analytics Platform

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Senior Machine Learning Engineer at AWS Identity Analytics Platform, focusing on building an AI-driven analytics platform that processes petabyte-scale data to generate insights for security and operational problems. The role involves designing, developing, and deploying ML solutions, including anomaly detection, time-series forecasting, classification, optimization models, and LLM-powered agents for conversational data querying. It also includes feature engineering, production deployment, and collaboration with leadership and service teams.

What you'd actually do

  1. 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.
  2. 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.
  3. Engineer features from petabyte-scale datasets using AWS services (Glue, Athena, EMR) and deploy models to production environments (SageMaker, ECS, EKS).
  4. 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.
  5. 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.

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • petabyte-scale data
  • state-of-the-art ML algorithms
  • LLM-powered analytics agents
  • design, develop, and deploy end-to-end ML solutions
  • petabyte-scale datasets
  • state-of-the-art techniques in ML, deep learning, and GenAI

Other signals

  • AI-driven analytics platform
  • ML algorithms to solve real-world security and operational problems
  • LLM-powered analytics agents
  • design, develop, and deploy end-to-end ML solutions