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.

Key Responsibilities

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
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Director of Product Strategy & Applied AI at name