About the role

The AI Economics Engineer is a specialist engineering role focused on optimising the cost, efficiency, and return on investment (ROI) of AI usage across the engineering organisation.
While comparable in seniority to a Software Engineering Specialist, this role differs in mission, skills, and ways of working. Instead of building features or platforms, the AI Economics Engineer designs the economic control plane for AI—making AI usage measurable, predictable, and financially sustainable at scale.
This role ensures that AI‑enabled delivery maximises business value per token, balancing speed, quality, and cost across models, agents, and SDLC stages.

What you’ll be doing

1) AI Cost Attribution & Transparency
•    Build token‑level cost attribution across the AI ecosystem, tracking usage by:  team / pod, agent, feature or workflow and  SDLC phase (design, build, test, review, run)
•    Ensure AI costs are visible, explainable, and attributable avoiding “black box” AI spend.
2) Model Selection & Routing Economics
•    Design and maintain model selection strategies that choose the cheapest model that meets quality thresholds for each task(smaller/faster models for linting or summarisation and larger models for architecture reasoning or complex design)
•    Partner with Platform Mesh Engineers to embed cost‑aware routing policies into model gateways.
3) Caching & Efficiency Optimisation
•    Implement and tune caching strategies to reduce redundant AI calls, including: semantic caching, prompt caching and response reuse where safe and appropriate
•    Identify opportunities to reuse outputs across pods, agents, and workflows without sacrificing correctness.
4) Productivity & Value Measurement
•    Define and track AI productivity metrics, such as: cycle time reduction, throughput per engineer, defect reduction / rework avoided, Tie productivity gains directly back to AI spend, enabling clear value narratives for leadership.
5) Cost–Quality Trade‑off Experiments
•    Run controlled experiments to evaluate cost vs quality trade‑offs: (second review pass by a larger model worth the additional cost? and Does summarisation reduce context size without harming output quality?)
•    Use evidence‑based findings to refine AI usage patterns and policies.
6) Budgeting, Forecasting & Leadership Insight
•    Forecast AI infrastructure and model consumption costs based on demand and roadmap assumptions.
•    Build dashboards and reports that provide leadership with: current spend, projected run‑rate, efficiency trends and optimisation opportunities
7) Waste Detection & Governance
•    Identify and remediate waste patterns, such as: unnecessarily large context windows, redundant agent loops and over‑provisioned model tiers
•    Establish guardrails and recommendations that prevent cost blowouts without slowing delivery.

Essential Skills / Experience

•    Strong experience in FinOps, cost optimisation, or economics‑driven engineering.
•    Solid grounding in data engineering and analytics, including pipelines and dashboards.
•    Strong understanding of LLM pricing models (tokens, throughput, batch, latency tiers).
•    Ability to translate technical metrics into clear business insights.

Desirable Skills / Experience

•    Background in software or platform engineering.
•    Experience working with AI platforms, agents, or model gateways.
•    Familiarity with experimentation frameworks and statistical reasoning.
•    Strong stakeholder communication skills, especially with leadership and finance partners.