What the role offers

• Design pragmatic solutions for real problems — assess each use case and select the right approach: data aggregation, visibility tooling, rule engines, workflow automation, or AI/ML. Not every problem requires AI; every solution requires justification.
• Rapid prototyping and iterative delivery — ship functional prototypes within days, validate value with real users, and iterate or kill based on outcomes. The first solution may not be the most optimal, but it will provide the much needed speed to build.
• Build agentic AI systems where justified — design and implement multi-agent architectures, autonomous workflows, and LLM-based tooling when the use case warrants the complexity and cost.
• Score and prioritise opportunities — standardise evaluation of use cases using impact, feasibility, data readiness, and time-to-value frameworks.
• Transition prototypes to production — partner with Platform Engineering to deploy validated solutions as scalable, monitored, production-grade systems.
• Build reusable infrastructure — create shared systems, templates, and patterns that enable departments to independently build and extend AI solutions over time.
• Synthesise findings into recommendations — deliver structured proposals with clear problem statements, technical approaches, projected outcomes, and success criteria.
• Justify cost, complexity, and value — document implementation economics for every solution, enabling leadership to make informed investment decisions. UI/UX is a critical consideration: deliver the right interface for the problem, not unnecessary complexity.
• Train and enable departmental teams — conduct hands-on sessions covering solution functionality, prompt engineering, usage patterns, and troubleshooting. Develop user guides, SOPs, and playbooks for all deployed solutions.
• Cultivate AI champions across the org — identify and develop power users in each department who become first-line support and internal advocates for AI adoption.
• Elevate company-wide AI literacy — help teams across the organisation understand what AI can and can’t do, how to use it effectively, and how to think about it as a tool in their daily work.

What you’ll bring to the role

• Deep understanding of AI/ML fundamentals — neural network architectures, generative AI mechanics, transformer models, and how these systems work at a technical level. This depth is essential for making sound build-vs-buy and AI-vs-simpler-approach decisions.
• Proficiency in at least one modern programming language with the ability to build solutions, automations, and data pipelines efficiently
• Deep working knowledge of Claude (including Claude Code CLI) — prompt architecture, tool-use patterns, and LLM-native application development
• Demonstrated experience building agentic AI systems: multi-agent orchestration, inter-agent handoffs, tool integration, and autonomous workflows
• Comprehensive understanding of token economics: context window optimization, cost management, rate limit handling, and efficiency-aware solution design
• Proven rapid prototyping ability with disciplined, hypothesis-driven experimentation and iterative development
• Strong cross-functional communication — deeply curious about how businesses operate; ability to translate between technical and non-technical audiences, deliver training, and present to leadership
• 3+ years software engineering experience with substantive focus on AI/ML, LLM applications, or automation engineering
• Experience with multi-agent frameworks (CrewAI, AutoGen, LangGraph) or equivalent custom agent architectures is a strong plus
• Familiarity with agent harness patterns: tool registration, context management, memory systems, and orchestration layers
• Experience with automation platforms (n8n, Zapier, Make) and API-first integration development is a plus
• Working knowledge of RAG architectures, vector databases (Pinecone, Weaviate, ChromaDB), and embedding-based retrieval
• Domain experience in FSM, enterprise SaaS, or workforce management; MCP server development; JavaScript/TypeScript; analytics platforms (QuickSight, Tableau, Looker) and SQL are all a plus
• First-principles problem solver: strong technical judgement with the ability to make pragmatic decisions, balancing long-term scalability with near-term delivery
  • Hustler mentality with engineering craft: resourceful, persistent, and pragmatic; you ship working systems, not just designs. Comfortable navigating ambiguity in a fast-paced environment