Director of Product Strategy & Applied AI

Department: IT

Employment Type: Full Time

Location: Remote- USA

Compensation: $234,000 - $260,000 / year

Description

Key Responsibilities

• 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 

• 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 

• 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 

• 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 — 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 

• 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 

• 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  

• 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 

Skills, Knowledge and Expertise

• 15+ years in product strategy, product management, solution engineering, or technology consulting, with 3+ years leading a team — hiring, coaching, performance, and capacity planning 
• Bachelor's degree in a technology- or business-related field; advanced degree a plus 
• Demonstrated hands-on technical depth with applied AI — enterprise LLM platforms (ChatGPT Enterprise, Claude Enterprise, Microsoft Copilot), agents, copilots, and automation — with prototypes or workflows you personally built and put in front of real users 
• Experience directing developers or engineers on prototype and MVP work — setting the technical direction, reviewing approach and output, and translating between practitioner need and build reality without owning the codebase 
• Working fluency across the stack this team uses: OpenAI, Azure OpenAI, and Anthropic APIs; Cursor or VS Code with GitHub Copilot; GitHub, Azure, and Postman; and agent tooling such as OpenAI Agents/SDK, Copilot Studio, or Power Automate 
• Experience running discovery in the field — sitting with practitioners, running project deep dives, and turning what you observed into documented use cases and testable prototypes 
• Proven ownership of a backlog and team capacity in Jira and Jira Product Discovery — prioritizing discovery, prototype, and enhancement demand against finite people, and communicating the tradeoffs upward and outward 
• Data fluency — SQL and modern data platforms (Databricks / Fabric), with Power BI for sizing, value measurement, and portfolio reporting 
• Strong business architecture and service design craft — service blueprints, personas, current- and future-state mapping, value stream analysis, worked in Miro, Lucidchart, and Figma — applied to real operating environments 
• Sound commercial judgment — pricing, cost-to-serve, ROI models, and buy-vs-build recommendations you have had to defend to a funding body 
• Track record influencing senior executives and operating leaders who do not report to you, and supporting investment decisions through a formal governance or council process 
• Ability to communicate at every altitude — a VP-level boss and executive sponsors, peer leaders in Product Management, Product Engineering, and Product Enablement, the team reporting up, and field practitioners — and to write the one-page recommendation an executive can decide from 
• Comfort in an early-stage function — setting standards and shaping the operating model as demand scales 
• Exposure to AEC, engineering services, or other physical-world operating environments preferred — with more weight on having built greenfield products than on deep familiarity with incumbent platforms (Autodesk, Bentley, Bluebeam, Trimble); working knowledge of where those systems hold the data is useful, but not the qualifying skill 

About Trilon