Engineer II (AI Enablement)

Build, tailor, and operate AI agents and assistants for internal functions, owning features end-to-end from design through deployment and production operation (including testing and CI/CD).

Build and maintain knowledge-base pipelines: collecting, structuring, and keeping current the content that AI systems rely on to be accurate and useful.

Integrate AI systems with company tools and data sources through well-structured, safe APIs and connectors.

Apply the crew's security guardrails and responsible-AI practices: access control, data privacy, and human-in-the-loop safeguards.

Build user-facing tooling and interfaces where the work calls for it, spanning UIs and the backend services behind them.

Instrument what you ship: adoption, quality, and cost metrics that show whether a solution is working.

Gather feedback from internal users, identify friction points, and turn them into concrete improvements.

Keep owned systems reliable, observable, and maintainable; participate in incident response and RCA for owned services.

Provide context and clarity through documentation and runbooks so others understand what's built and why.

Contribute to the crew's shared frameworks and to AI-assisted engineering practices a

3+ years of professional engineering experience.

Experience building and operating production systems end-to-end (services, APIs, or tooling).

Hands-on experience building with LLMs (features, agents, automations, or serious side projects with production-quality practices).

Understanding of system design, databases/data modelling, and application architecture.

Experience with agent frameworks, MCP-style tool interfaces, RAG, or knowledge-base systems.

Experience integrating third-party SaaS APIs.

Full-stack experience — building web UIs as well as their supporting backend services.

Experience building internal or developer-facing tools.

Exposure to LLM evaluation, observability, or prompt/context engineering.