AI Architect

The clearest sign this role is working: AI initiatives at Citation start well and stay on track architecturally. In practice that means:

Established patterns are the default starting point for new builds, not something teams discover after the fact

Design questions are resolved before Engineering starts building, not during or after

Third-party Statements of Work are assessed architecturally before they're signed

Model and hosting choices are made against a documented decision framework, not habit or vendor pressure

Token spend and cost-per-outcome are tracked and explainable, not a surprise on the invoice

Security is involved in every significant initiative from the start, not introduced at the end

Leadership has a current, accurate view of AI architectural risk and direction, with no significant surprises

Hold a documented, evidence-based framework for choosing between closed frontier models accessed through a provider's API (Anthropic, OpenAI, Google) and open-weight models run on Citation's own infrastructure (Llama, Mistral, Qwen and similar) — and apply it per workload, not as a single blanket choice. The framework should weigh:

Task complexity — complex reasoning and long-horizon agentic work generally favour frontier closed models; simpler classification, extraction, and templated generation are often better served by smaller open-weight models at a fraction of the cost

Volume and cost at scale — at high token volumes, self-hosting open-weight models can cross into materially cheaper territory; this role owns the analysis of where that break-even sits for Citation's actual workloads, not a generic industry number

Data sensitivity and residency — for HR, employment, and compliance data, self-hosted open-weight models remove a class of third-party data-sharing risk that closed providers (even with strong contractual terms) don't fully eliminate

Fine-tuning and customisation — where an open-weight model fine-tuned on Citation's own data would outperform a general-purpose model at lower running cost, that's a build case this role should be able to make with evidence, not intuition

Latency, licensing, and total cost of ownership — including the practical overhead of running and maintaining your own models, not just the sticker price of tokens

Understand and actively manage the unit economics of every AI solution recommended, including:

Modelling likely token cost at design time, before a system is built, so the business knows what it's signing up for

Specifying model routing (cheaper models for simpler steps, frontier models reserved for what actually needs them), prompt and context engineering for efficiency, and caching where appropriate

Recognising that agentic and multi-agent patterns can multiply token consumption several-fold over a single well-scoped call, and that orchestration-pattern choice is itself a cost decision

Setting up the observability to track cost-per-outcome and cache performance over time, not just the total spend line

Assess whether the data behind any AI initiative is structured, accessible, and reliable enough to support the intended behaviour — across Citation's Salesforce, Atlas, Snowflake, and integration layers — and flag readiness issues before they become delivery blockers.

Hold the approval path for new AI architectural patterns and material departures from existing standards. Attend initiative design conversations early enough to shape them, not just review them, and sign off on the AI design elements of partner Statements of Work.

Define scope and success criteria for AI spikes and proofs of concept before they start, validate vendor and partner capability claims before they're embedded in a committed design, and make sure proof-of-concept outputs are evaluated against real delivery constraints — not vendor demo conditions.

Act as the internal architectural counterpart to Citation's delivery partners, the way any enterprise architecture function holds its critical vendors to a defined standard: review proposals and Statements of Work before commitments are made, run design reviews during delivery, and give partners a well-defined target with technical challenge where their decisions need scrutiny.

Set the standard for how AI systems are monitored and evaluated once live — offline and online evaluation, drift detection, guardrails — and own the response framework for AI incidents in production, alongside the cost governance described above.

Bring Security in as a standing stakeholder on every significant AI initiative, addressing data boundaries, personal data handling, prompt injection risk, and third-party model provider assessments early rather than at the end. Maintain alignment with the ISO 27001 information security standard and data protection law, with particular care where personal employment or HR data is used as model input — this is where the open-weight-vs-closed decision above earns its keep.

Define and maintain guardrails on what AI systems should and shouldn't decide autonomously, ensure outputs are explainable where they affect clients or employees, assess new initiatives for bias risk, and track emerging responsible-AI regulation for its implications on Citation's systems.

Run informal sessions or working groups to build AI literacy across Engineering, Product, and the Business. Be a first point of contact for AI questions and support onboarding of technical staff into Citation's AI standards.

Monitor the AI landscape — including the open-weight ecosystem specifically, given how fast it's moving — and evaluate new tools, models, and platforms against Citation's actual needs before they gain internal momentum on hype alone. Give the Chief AI Officer a clear, current view of AI architectural risk and direction across the portfolio.