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About This Role
### Description
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
- 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
### Skills, Knowledge and Expertise
Requirements / Qualifications* 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
Trilon was formed with the vision of building the next Top 20 infrastructure consulting firm in North America by bringing together some of the nation’s best infrastructure consulting firms, focused on delivering practical and sustainable infrastructure solutions. Trilon is backed by Alpine Investors, a PeopleFirst Private Equity Firm. Trilon currently comprises 5,500\+ staff across the US. For more information, visit www.trilon.com.
Pay Transparency
The base salary range for this role is indicated in the posting. This range reflects the company’s good faith estimate of the compensation for this position at the time of posting. Final compensation will be determined based on factors such as experience, skills, qualifications, internal equity, and geographic location.### About Trilon Group
Trilon group was formed with a vision to build the next Top 20 design firm in North America with a reputation for delivering smart and sustainable infrastructure solutions. Our investment in talent ensures we are the most trusted partner by our clients, our talent, and our investors.
Similar to the infrastructure we design, we want to build an enduring company for our clients, our people, and the communities we serve. We invest in partners who ensure infrastructure solutions address some of the communities most complex challenges of sustainability, resiliency, social equity, and constructibility.
As a People First company, we are focused on growing the careers of our people faster within the Trilon group than our peers. We invest heavily in developing and elevating talent across our family of companies. Trilon Group offers a multitude of career paths, spanning technical, project management, business management, business development, and operations.
Salary Context
This $234K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Trilon Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($247K) sits 15% above the category median. Disclosed range: $234K to $260K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Trilon Group AI Hiring
Trilon Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, Salt Lake City, UT, US. Compensation range: $190K - $260K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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