About this role
Trilon is a family of leading engineering and professional services firms reshaping how the AEC industry works. Our partner firms design and deliver the roads, water systems, and public spaces communities depend on every day — and we pair that talent with modern technology, data, and AI to do the work faster, smarter, and at greater scale. Our mission is to make the work of building the physical world dramatically better for the people who do it and the communities who use it. Inside that mission sits the AI Innovation & Applied Technology team — the R&D engine that runs discovery in the field, builds working prototypes, and turns proven ideas into funded products. Product Strategy is the front end of that engine, and field engineering is how it earns its evidence. This role leads Trilon's product strategy practice, in close partnership with AI Field Engineering and Solution Architecture. It runs the team that goes into the field, finds where the work is slow or manual, prototypes against it with those partners, and proves what is worth building — and it runs the mechanics that keep the team productive: capacity, backlog, deep dives, the idea tracker, field use cases, and enhancement demand from live products. It is a hands-on seat: the Director prototypes, reads code, tests agents, and works in the stack alongside the team, not only reviews it. The role builds the evidence investment decisions rest on — discovery findings, prototypes, feasibility and market input, sizing, and the value hypothesis — and brings it to Investment Council. Team Leadership and Capacity Team leadership — hiring, ramp, performance, and coaching for a team of product strategists and business architects Capacity management across the product strategy team, with tradeoffs made visible rather than quietly absorbed Cross-functional partnership with AI Field Engineering and Solution Architecture on prototype, feasibility, and architecture work Judgment on the hard calls — a value hypothesis that will not hold, a prototype drifting into production, a sponsor set on a predetermined answer Operating cadence — discovery readouts, prototype demos, and backlog reviews that compound learning across the firms Discovery and Field Intelligence Enterprise and product discovery, end to end — from field signal through to the synthesis that separates a pattern from a one-off Project deep dives — planned, staffed, and documented so each one ends in findings and a decision Idea tracker run as a working funnel in Jira Product Discovery — capture, triage, sizing, disposition, and visible status Field use-case library in Confluence or SharePoint — where AI is applied across the firms, what worked, what is reusable Market and vendor scan with Field Engineering and Solution Architecture — what is buyable, what is already in the stack, what has to be built Field relationships with operating-company presidents, practice leaders, and IT that keep signal flowing continuously Applied AI and Prototyping Field-based design — time with practitioners, designing against observed work rather than theory Prototype scoping — future automations, applications and agents (OpenAI Agents/SDK, Copilot Studio, Power Automate) aimed at the riskiest assumption Hands-on technical fluency — enterprise LLM platforms, the OpenAI, Azure OpenAI, and Anthropic APIs, and AI-assisted development in Cursor or VS Code Technical direction of developers — framing what gets built, pressure-testing the approach, reviewing the work, and knowing enough of the build path to hold a prototype honest on effort, risk, and reuse Working conventions with Field Engineering and Solution Architecture — GitHub, reusable components, Azure environments, and the line between throwaway and production Greenfield build instinct — designing new applications, agents, and data products from a blank page rather than extending incumbent AEC platforms, with prototypes instrumented so usage, latency, and failure are measured, not assumed Backlog and Intake A single prioritized backlog in Jira — discovery requests, deep dives, prototype work, and enhancement demand from live products Prioritization and sequencing against capacity, re-sequenced openly and groomed so every item has an owner and a next step Enhancement triage — defect, enhancement for Product Management, or new opportunity worth discovery Business Architecture and Service Design Business architecture — service blueprints, personas, current-state architecture, and value stream mapping in Miro, etc Future-state service design — the target practitioner and client experience, tested as a Figma concept before a story is written Shared Services orchestration — Solution Architecture, Data Engineering, Cybersecurity, UI/UX, CAD, Platform Engineering — scoped to inform the work, not become a build Early feasibility calls — data availability in SQL, Databricks or Fabric, integration surface, and Azure cost to run Business Case and Investment Evidence Evidence packages per opportunity — problem framing, prototype results, sizing, ROI logic in Power BI, and the value hypothesis Pricing, cost-to-serve, and financial modeling, including buy-vs-build recommendations with assumptions stated plainly enough to be argued with Investment Council material — the recommendation, the math behind it, and the follow-ups when work comes back for sharpening Intake discipline — parking opportunities that lack a clear owner, a real value hypothesis, or evidence Success measures and stage gates set at approval, so a funded bet can be judged against what was promised Handoff and Value Realization Clean handoff to Product Management — intent, scope, and value hypothesis confirmed at approval Availability through the build without taking the wheel — Product Management and Product Engineering decide when and how the work ships Value realization with Product Enablement — baseline agreed before launch, adoption and in-market data read against it, and an honest assessment when a bet did not move the needle Feedback loop — adoption gaps, workarounds, and enhancement requests routed back into discovery, the idea tracker, and the backlog Domain Maturity and Enablement Practice standards in Jira, Confluence , etc — discovery method, deep-dive format, prototype conventions, sizing, and financial modeling Playbooks and onboarding that let the function scale across the family of firms without re-inventing itself for each operating company Representation to AI Innovation & Digital Products leadership, executive sponsors, IT leadership, and partner firms — including demos and field sessions that show rather than describe