Senior Technical Specialist, AI Governance

Eaton Corporation•Raleigh, NC

8d•$130,000 - $190,000•Hybrid

About The Position

Eaton’s Corporate Sector division is currently seeking a Senior Technical Specialist, AI Governance. This position can sit out of any of our US Eaton locations on a hybrid schedule. The expected annual salary range for this role is $130000 - $190000 a year. This position is also eligible for a variable incentive program. 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. 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.

Requirements

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.
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

Nice To Haves

Master's degree in Computer Science, Machine Learning, or a related field.
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, MCP or equivalent context-layer and agent-to-agent sharing patterns
DevOps & SRE: CI/CD, Infrastructure-as-Code (Terraform or Bicep), Observability with OpenTelemetry, metrics, logs, traces, SLO/SLI design and incident response
Security & Governance: OAuth and OIDC, secrets management, PII and PCI handling, secure SDLC, OWASP standards, Policy-as-Code patterns
AI FinOps: Token, credit, and license accounting, Cost attribution by use case, model routing, caching strategies, Cloudability and Azure OpEx familiarity
LLM Lifecycle: Prompt versioning, eval design, accuracy and quality scoring, Drift and hallucination monitoring, Red-teaming and adversarial testing basics, Familiarity with foundation models (OpenAI, Anthropic Claude, and peers)
Leads technically without formal authority; earns influence through code, design, and clarity.
Translates policy into working software, not slide-ware.
Partners deeply with Cyber, Legal, Architecture, and Responsible AI & Oversight without waiting to be invited.
Bias to ship; balances speed with control and knows when to slow down.
Communicates trade-offs clearly to non-technical stakeholders and executives.
Coaches engineers with patience and directness; grows the pod as they build the product.
Thinks Big, Acts Bold, Wins Together, in Eaton's plain-English form: takes smart risks, decides with conviction, and lifts the team.

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.