Artificial Intelligence Engineer

Role Purpose

The ceiling of AI adoption in any organization is set by how much its senior leaders use it. The AI Engineer works alongside a functional leader full-time, understands their work, and builds AI-powered tools with them on a continuous basis. The role combines the ability to earn trust with senior stakeholders, scope ambiguous problems into buildable pieces, and ship functioning solutions quickly using modern AI tools.

Key Responsibilities

1. Opportunity Discovery (25%)

Conduct structured conversations with the assigned functional leader and their teams to understand workflows and surface AI opportunities. Translate half-formed ideas into scoped prototypes with clear problem statements and success criteria.

2. Rapid Prototyping and Delivery (35%)

Build and deploy AI-powered tools, agents, automation pipelines, and dashboards on a weekly cadence. Work across documents, email, chat, forms, and spreadsheets using AI coding tools, automation platforms, APIs, and agentic frameworks. Default to the simplest AI-native solution that delivers meaningful value.

3. Stakeholder Advisory (15%)

Challenge the functional leader's thinking. Suggest what they are not considering and flag approaches that will create problems downstream. Reframe requests toward outcomes rather than features. Earn credibility through delivered results and clear communication.

4. Systemization and Scaling (15%)

Convert successful builds into reusable templates, playbooks, and tools that teams can operate independently. Maintain prompt libraries and automation catalogs. Track adoption and iterate based on real-world usage.

5. AI Quality and Methodology (10%)

Apply disciplined AI development practices: precise specification before building, verification through edge-case testing and evaluation sets, and systemization of wins for reuse. Plug into existing enterprise AI infrastructure managed by CPTO and CIO teams as needed.

Required Skills and Experience

Stakeholder and Scoping Skills

• 4-8 years of professional experience with demonstrated ability to earn trust with senior stakeholders and scope ambiguous problems into buildable solutions
• Strong business orientation: thinks in workflows, outcomes, and organizational impact
• Effective communicator who can challenge senior leaders constructively and deliver clear documentation

Technical Skills

• Expert-level fluency with the current state of AI automation and agentic coding, including AI coding tools (Cursor, Claude Code, Codex), automation platforms (N8N, Make), skills, plugins, MCP integrations, and agentic frameworks
• Ability to build functioning automations, dashboards, AI skills, and AI wrapper products using AI-assisted development
• Foundational knowledge of product management, system architecture, and software engineering sufficient to make sound scoping and design decisions
• Comfortable with APIs, JSON, webhooks, and basic data storage

Working Style

• Works in short cycles: demos early, iterates based on real usage
• Writes lightweight documentation for everything shipped
• Keeps priorities visible and escalates trade-offs promptly