Applied Scientist: Agentic AI for Audit & Risk

Amazon; Arlington (VA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon is seeking an Applied Scientist to accelerate the growth of Internal Audit’s Data Science & Risk Intelligence initiatives. The role focuses on building ML and generative AI solutions to expand self-service data usage for audit teams, leveraging Bedrock, SageMaker and related tools.

You will own end-to-end problems, from framing to measurable impact, in a fast-paced environment. The ideal candidate has strong ML/LLM expertise, production-level infrastructure experience on AWS, and a track

Qualifications

• 3+ years of building models for business applications.
• PhD, or Master’s with 4+ years in CS/CE/ML or related field.
• Proficiency in Java, C++, Python or similar languages.
• Experience with ML fundamentals, LLMs, model training/inference, or building complex software delivered to customers.
• Experience architecting/operating AWS-based solutions.

Responsibilities

• Partner with audit teams and engineers to define and deliver ML/AI products end-to-end.
• Design and own agentic AI systems and evaluation methods.
• Analyze large datasets using SQL and Python/R to derive insights.
• Architect secure, scalable AWS ML solutions from design to production.
• Maintain production ML infrastructure, pipelines, observability, and experiments.
• Drive applied research and disseminate findings via publications/talks.
• Mentor junior scientists and influence the product/technical roadmap.

Skills

Java

C++

Python

ML fundamentals

LLM concepts

AWS

Data structures

Education

PhD or Master's in CS/CE/ML

Tools

Amazon Bedrock

SageMaker

LangGraph

Strands

Job description

Amazon is seeking an Applied Scientist to accelerate the growth of Internal Audit’s Data Science & Risk Intelligence initiatives. The role focuses on building ML and generative AI solutions to expand self-service data usage for audit teams, leveraging Bedrock, SageMaker and related tools.

You will own end-to-end problems, from framing to measurable impact, in a fast-paced environment. The ideal candidate has strong ML/LLM expertise, production-level infrastructure experience on AWS, and a track