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information, please see:
https://illinoisjoblink.illinois.gov/jobs/13755739
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 workstreamtranslating 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)*
Evaluation, Testing & Release Quality (25%)\
- 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 toolchains, 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)*
Model/Prompt Routing Contributions (15%)\
- 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)*
Integration with Tools and MCPs (15%)\
- Lead implementat on 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 secure-by-design tool access (least privilege, logging, PII handling, policy enforcement). *(Consult/Execute)*
Operational Readiness, Collaboration & Continuous Improvement (10%)\
- Ensure production readiness for owned components: telemetry coverage, structured logging, traceability for tool calls, SLIs/SLO alignment (latency, success rate, cost), and participation in incident response and postmortems. *(Execute/Consult)*\
- Proactively identify delivery risks (dependencies, rate limits, data quality, security scopes, vendor constraints) and drive resolution with clear tradeoffs and recommendations. *(Consult/Influence)*\
- Mentor peers through technical leadership: raise code quality, share patterns, review PRs for correctness/performance/security, and contribute to internal playbooks. *(Influence)*
Decision-Making Autonomy: High-moderate significant autonomy in AI engineering design choices and evaluation approach; aligns w