Senior Software Engineer, AI Governance

Eaton Group

Moon Township (Allegheny County)

Sur place

USD 130 000 - 190 000

Plein temps

14 jours+

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Résumé du poste

Eaton is seeking a Senior Software Engineer, AI Governance to design and build AI-native applications for internal use across the AI Governance Office. The role leads a pod of engineers, writes production code, and sets engineering standards, mentoring a small team.

This hands-on IC role focuses on the AI Control Tower, AI Funnel, and FinOps tooling, collaborating with Cyber, Legal, Privacy, and Responsible AI to turn policy into working software.

Qualifications

10+ years of software engineering experience.
3+ years building LLM and agent-based production systems.
No relocation; must reside within 50 miles of US Eaton location.
Authorized to work in the United States without sponsorship.

Responsabilités

Set the technical direction and engineering practices as a hands-on IC.
Design, build, and run AI Control Tower, AgentOps, AI Funnel, and FinOps capabilities.
Engineer observability and compliance across Eaton's AI estate, including post-approval monitoring.
Translate AI policy and risk requirements into working software with cross-functional partners.
Own architecture for complex, integrated systems and ensure scalable, secure solutions.

Connaissances

LLM patterns

Agent frameworks

Observability

Policy-as-Code

Formation

Bachelor's degree in CS or related

Master's degree preferred

Outils

Azure

Docker

Kubernetes

Description du poste

Eaton’s Corporate Sector division is currently seeking a Senior Software Engineer, AI Governance. The expected annual salary range for this role is $130000 - $190000 a year.

Please note the salary information shown above is a general guideline only. Salaries are based upon candidate skills, experience, and qualifications, as well as market and business considerations.

What you’ll do:

Job Summary:

This is a full-time software engineering position focused on designing and building AI-native applications and platforms for internal use across Eaton's AI Governance organization. The role will contribute to strategic initiatives including AI Intake, AI FinOps, AI Catalog, and other AI Governance solutions. This is a hands‑on senior individual contributor who leads the technical direction of the AI‑Native Software Engineering pod inside Eaton's Data & AI Governance Office. The role designs, builds, and runs the platforms behind Eaton's AI Governance program, including the AI Control Tower, AgentOps, the AI Funnel, and AI FinOps tooling. The Pod Lead writes production code, sets engineering standards, and mentors a pod of three engineers, partnering with AI Architects, Solution Architecture, Cyber, Legal, Privacy, and Responsible AI & Oversight to turn policy into working software.

Key outcomes:

AI Control Tower MVP live in production, covering runtime signals, agent inventory, and unified audit log across Copilot Studio, AI Builder, and Power Automate.
AgentOps observability in production for the priority agent set, with tracing, evaluation scoring, and drift and hallucination monitoring wired into incident response.
AI FinOps dashboard reporting token, credit, and vendor-license spend by use case, with attribution back to owners.
Policy-as-Code framework live for the top 5 AI policies, with automated evidence collection into the AI Funnel intake.
Post‑approval lifecycle monitoring stood up, closing Eaton's #1 AI maturity gap with per‑use‑case telemetry and named owners.
Pod operating on a healthy CI/CD, IaC, and SLO discipline, with published engineering standards adopted across both build pods.
Vendor and embedded AI telemetry ingested from ServiceNow, MuleSoft, Agentforce, and GitHub Copilot into the Control Tower for coverage reporting.

Job Responsibilities:

Set the technical direction, design standards, and engineering practices for the pod as a hands‑on IC, contributing production code every sprint.
Design, build, and run the AI Control Tower, AgentOps, AI Funnel, AI FinOps, and Policy-as-Code capabilities that power Eaton's AI Governance program. [AIDG Capab…Org Model | PowerPoint]
Engineer observability, monitoring, compliance, and alerting across Eaton's AI estate, including post‑approval lifecycle monitoring. [eaton‑my.s…epoint.com]
Build with AI‑native patterns first: LLM apps, agents, RAG, and automated workflows as core components, not bolt‑ons.
Translate AI policy and risk requirements into working software, partnering with Cyber, Legal, Privacy, and Responsible AI & Oversight. [eaton‑my.s…epoint.com]
Own architecture and design for complex, integrated systems: interfaces, data structures, storage, integrations, and deployment.
Set and defend non‑functional requirements: scalability, performance, reliability, security, and cost.
Manage the LLM lifecycle for pod‑built solutions: prompt versioning, eval design, accuracy scoring, and drift and hallucination monitoring. [eaton‑my.s…epoint.com]
Optimize token, credit, and cloud consumption; design model routing, caching, and cost attribution into every use case. [eaton‑my.s…epoint.com]
Ingest telemetry from embedded and vendor AI (ServiceNow, MuleSoft, Agentforce, GitHub Copilot) into the AI Asset Catalog and Control Tower. [eaton‑my.s…epoint.com]
Provide dotted‑line technical leadership to three engineers: design reviews, code reviews, coaching, and technical growth planning.
Run a continuous delivery pipeline with secure SDLC, IaC, and automated testing baked in from day one.

Qualifications:

Basic (required) Qualifications:

Bachelors' degree from an accredited institution
Minimum ten (10) years of software engineering experience, with three (3) years hands‑on building LLM and agent‑based production systems.
No relocation is offered for this position. All candidates must currently reside within 50 miles of US Eaton location.
Must be authorized to work in the United States without company sponsorship now or in the future

Preferred Qualifications:

Master's degree in Computer Science, Machine Learning, or a related field.

Technical knowledge:

AI‑Native Engineering

LLM application patterns, agent frameworks (LangGraph, AutoGen, Semantic Kernel, or equivalent)
Retrieval‑augmented generation, prompt engineering, evaluation harnesses
Guardrails, trust and safety patterns, red‑teaming basics
On‑Behalf‑Of authorization patterns for agents

Cloud & Platforms