The role

Wilkes & Liberty needs an AI / Agentic Systems Engineer for work where AI is part of the delivered system, not merely an engineering aid. You will own the path from a bounded use case to production behavior: model and retrieval choices, tool authority, evaluation, guardrails, observability, cost, and operator control.

What you will own

Define the user decision, system boundary, success criteria, unacceptable outcomes, and operator-approval points before selecting a model or orchestration pattern.
Design and implement retrieval, structured generation, tool use, workflows, and Model Context Protocol integrations with least-privilege authority and explicit data boundaries.
Evaluate model, provider, and deployment options against quality, privacy, sovereignty, latency, availability, and inference-cost requirements.
Build representative evaluation sets and automated checks for accuracy, groundedness, refusal behavior, prompt injection, unsafe tool calls, data leakage, and regression.
Engineer guardrails, schema validation, authorization, rate limits, budgets, audit records, and fail-closed behavior around probabilistic components.
Instrument model, retrieval, tool, cost, and operator-review behavior so failures can be diagnosed and acceptance claims can be reproduced.
Plan fallback, rollback, incident response, and change control for model, prompt, retrieval, and tool updates.
Transfer the system with versioned configuration, tests, evaluation evidence, runbooks, known limits, and clear operator controls.

What you bring

You have built or operated an AI-enabled product or workflow where model output affected a real user, decision, or system action.
You can design retrieval and tool-using systems in Python, TypeScript, or a comparable stack and reason about the authority granted at each boundary.
You treat evaluation as engineering: you define datasets, criteria, thresholds, failure classes, and regression evidence rather than relying on a compelling demonstration.
You understand model limitations, prompt injection, untrusted content, privacy, access control, observability, and operator oversight.
You can compare hosted and self-managed models without assuming that the newest or largest model is the correct operational choice.
You explain quality, cost, latency, risk, and operator-control tradeoffs plainly to technical and non-technical stakeholders.

Helpful experience

Model Context Protocol, tool calling, structured outputs, retrieval-augmented generation, search and vector retrieval, and evaluation frameworks.
Open-source or self-hosted models, private infrastructure, air-gapped constraints, and provider-independent system design.
Identity-aware agent actions, operator-gated workflows, audit logging, and governed access to content or operational systems.
Federal, regulated, or high-assurance environments where data residency, documented evaluation, and bounded automation determine whether the system is usable.

Equal opportunity and accommodations. Wilkes & Liberty considers qualified candidates without regard to protected characteristics. Reasonable accommodations are available during the application process.