AI Engineer

Overview

As an AI Engineer specializing in Agentic AI enablement, you will participate in the design and delivery of production-grade agent capabilities built on the enterprise AI Backbone across cloud and edge environments – across supply-chain and global functions. You will be responsible for end-to-end delivery of key agent modules and integration patterns (MCP/tooling), establish strong evaluation and regression discipline, and drive adoption by partnering with transformation teams, BU, platform engineering, and enterprise application owners. You serve as a technical engine for the workstream—translating business workflows into measurable agent outcomes, working to mitigate identified risks, evaluating/experimenting with options/tradeoffs, and working to scale solutions across domains.

Responsibilities

• Agent Engineering & Workstream Delivery (35%)
  • Lead design and productionization of high-leverage agent modules and reusable patterns (tool-use orchestration, policies/guardrails, memory, RAG where it adds measurable value), built as composable components and reference implementations.
    • (Execute/Lead)
    • Translate ambiguous product/problem statements into concrete agent behaviors and system designs: state models, failure modes, tool contracts, latency budgets, and acceptance criteria that engineering + product can execute against.
      • (Execute/Consult)
      • Deliver quickly without sacrificing quality: create thin vertical slices, iterate with evidence, and converge on robust behavior under real-world constraints.
        • (Execute)
        • Drive meaningful performance gains via systematic optimization: latency, token efficiency, tool-call success, retrieval quality, and cost per successful task, including remediation of long-tail failure modes.
          • (Execute)
          • Proactively identify platformizable opportunities: refactor one-off implementations into shared frameworks/SDKs that reduce build time for others.
            • (Execute/Influence)