Engineer II

Key Responsibilities

• 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

Experience & Background

Essential:

• 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.
• Familiarity with cloud infrastructure and CI/CD pipelines.
• Strong problem-solving and collaboration skills, with a user-centric mindset.
• Proficiency in Python and/or TypeScript.
• LLM application development: model APIs, prompt/context engineering, basic RAG patterns.
• API design and data integration across multiple systems and sources.
• Database design, SQL, and data modelling.
• Familiarity with cloud services (AWS preferred), containerisation (Docker/Kubernetes), and CI/CD practices.

Desirable:

• 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.