Senior Software Engineer, AI Governance

Overview

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.
• Engineer observability, monitoring, compliance, and alerting across Eaton's AI estate, including post-approval lifecycle monitoring.
• 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.
• 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.
• Optimize token, credit, and cloud consumption; design model routing, caching, and cost attribution into every use case.
• Ingest telemetry from embedded and vendor AI (ServiceNow, MuleSoft, Agentforce, GitHub Copilot) into the AI Asset Catalog and Control Tower.
• 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.

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: Microsoft Azure (Eaton primary), AKS, Docker, serverless.
• API design, event-driven architecture.
• Familiarity with AWS or GCP a plus.
• Languages & Stacks: Python (primary), TypeScript or JavaScript, SQL.
• Familiarity with .NET or Java a plus.
• Modern software practices: microservices, APIs, test automation.
• Data & Integration: Snowflake, Azure Data Factory, event streaming.
• REST and GraphQL API patterns.

Benefits

• Eaton provides various Health and Welfare benefits as well as Retirement benefits, and several programs that provide for paid and unpaid time away from work.
• Specific programs and options available to an employee may depend on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements.

About Eaton