Forward Deployed AI Integrator , Field Engineering
Description
Field Engineering teams across AWS are building AI-enabled workflows to accelerate investigations, automate repetitive tasks, improve reporting, and develop scalable engineering tooling. These efforts are happening organically — driven by motivated engineers across regions. The opportunity now is to systematically scale what's working and embed dedicated AI integration capability directly within each regional Field Engineering team.
The Forward Deployed AI Integrator will be 100% focused on AI integration for their assigned region. This role embeds directly with regional FE teams to identify high-value workflow opportunities, drive hands-on adoption of AI-assisted tooling, and convert successful experiments into repeatable regional standards. The role partners with FE engineers, regional leads, and cross-functional GenAI platform teams to deliver measurable productivity outcomes within the region.
This is a hands-on execution role — not advisory. Success is measured by workflows transformed, engineering hours saved, and adoption driven within the region.
Key job responsibilities
Embed directly with regional Field Engineering teams to identify, prioritize, and accelerate the highest-value AI workflow opportunities
Drive adoption of AI-assisted workflows across investigations, reporting, operational analysis, and engineering tooling within the assigned region
Partner with FE engineers to replace manually intensive workflows with scalable, reusable AI-enabled solutions
Build and document reusable workflow patterns, templates, and lightweight operational playbooks for regional FE adoption
Work with GenAI platform teams and internal tooling teams to accelerate delivery of FE AI initiatives within approved deployment environments
Coach and enable FE engineers on practical, approved AI tools including Python-based tooling, Kiro, and internal AI platforms
Track and report adoption metrics and operational KPIs to regional and senior FE leadership
Identify opportunities to eliminate duplicated effort and share reusable capabilities across regional FE teams
Contribute to the broader FE AI integration community by sharing learning, patterns, and outcomes across regions
Support development of FE-led initiatives including waveform analytics, reporting automation, engineering tooling, and operational dashboards
A day in the life
You are embedded in the regional Field Engineering team — working alongside engineers during live investigations, building automation patterns, and coaching ICs on workflow adoption. You spend your time identifying what's slowing engineers down, prototyping AI-assisted solutions, and driving adoption of what works.
You are a dedicated member of the regional team, accountable for making AI integration real and measurable. You bring technical depth, operational curiosity, and a bias for action. You move fast, learn from what you build, and share what works with the broader FE AI integration community.
You are known in your region as the person who makes AI practical — and delivers it.
About the team
Field Engineering supports critical operational functions across global AWS infrastructure. The organization spans multiple engineering disciplines and regions — covering operational investigations, electrical and mechanical systems, tooling development, reporting, commissioning, troubleshooting, operational analytics, and process improvement.
The team is actively building AI-enabled workflows to improve operational efficiency and accelerate engineering execution. This role is one of three initial regional AI integration positions, designed to prove the model and scale it across all FE regions.
Basic Qualifications
- Experience managing technical programs across cross-functional teams, building processes and coordinating release schedules
- Experience building and evaluating system-level technical design
- Bachelor's degree, or a Master's degree and experience with various machine learning techniques and parameters that affect their performance
- Hands-on experience in Physical AI, encompassing areas such as Computer Vision and/or Robotic systems.
Preferred Qualifications
- Knowledge of critical data center mechanical and electrical equipment
- Knowledge of AWS services
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