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.

• 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)

• Define and implement evaluation strategies for assigned workflows:
  golden sets, scenario coverage maps, regression suites, online/offline metrics, and release gating thresholds aligned to real business outcomes. (Execute/Consult)
• Build repeatable evaluation systems (templates, labeling guidance, dataset/versioning conventions, dashboards/reports) so evaluation becomes a productized capability
  , not ad hoc testing. (Execute/Lead)
• Implement robust automated testing across layers: unit tests for prompt/tool wrappers, contract tests for tool schemas, integration tests for tool chains, and agent simulation tests for multi-step flows. (Execute)
• Lead root‑cause analysis of quality failures (hallucinations, tool misuse, retrieval misses, routing errors): isolate causes (prompt/tool/data/model), implement corrective actions, and prevent regressions. (Execute)
• Champion evidence-first iteration: decisions and releases are backed by eval results, not gut feel. (Influence)

• Contribute to router design and task-to-model mapping through routing rules/classifiers, prompt strategies, and model selection policies; validate decisions using evaluation data and runtime telemetry. (Execute/Consult)
• Propose and implement routing improvements when constraints change (pricing, latency, throughput, new model capabilities), with governance-aware rollouts and rollback plans. (Consult/Execute)
• Identify and mitigate routing failure modes (over-escalation to expensive models, under-routing causing quality loss, brittle heuristics) and improve robustness using lightweight ML or rules where appropriate. (Execute)

• Lead implementation of MCP connectors/clients for enterprise apps and internal data products with strong engineering hygiene:
  schema/versioning discipline, typed contracts, scopes/permissions, auditability, and integration test strategy
  . (Execute/Consult)
• Build reusable integration patterns: standardized tool metadata, error normalization, retries/timeouts, idempotency, pagination handling, and consistent auth patterns to accelerate onboarding of new tools. (Execute)
• Collaborate with security/data owners to ensure…