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About This Role
### Description
Trilon is building a supercharged, technology\-enabled future for our people and partners. The Applied AI Engineer plays a critical role in that mission by building the AI\-powered features that enable our tools to compress real engineering labor across our operating companies.
This role sits at the intersection of software engineering and applied AI, focused on designing and implementing the intelligence layer of our products. You translate product requirements and architectural patterns into working AI capabilities by building prompt frameworks, retrieval\-augmented generation pipelines, and agent\-based workflows that operate against real engineering data and deliverables.
Working within a product pod, you partner closely with the Lead Engineer, Software Engineer, and QA Engineer to deliver production\-ready solutions. You own how the system reasons, including prompt design, context management, model integration, and orchestration logic. You also help define how quality is measured for AI outputs, ensuring tools are accurate, reliable, and usable in real\-world workflows.
You will engage directly with engineers across our operating companies to understand workflows, validate solutions, and iterate quickly based on feedback. You may also participate in field\-based project hackathons, embedding with teams to identify high\-impact opportunities and rapidly prototype solutions that inform platform development.
This role requires strong software engineering fundamentals, deep hands\-on experience with modern AI tooling, and the ability to operate in a fast\-moving environment where both the technology and the product are evolving. You are comfortable with ambiguity, rigorous about output quality, and focused on delivering AI that engineers trust and use.
### Key Responsibilities
AI Application Development* Design and build AI\-powered features using large language models and related tooling
- Develop and maintain prompt architectures that drive consistent, high\-quality outputs
- Implement retrieval\-augmented generation pipelines using enterprise data sources
- Build and orchestrate agent\-based workflows to automate targeted tasks
Model Integration and System Behavior* Integrate LLM APIs such as Anthropic Claude and OpenAI into production systems
- Design context management strategies to ensure outputs are grounded, relevant, and accurate
- Manage tradeoffs across latency, cost, and performance in AI workflows
- Continuously improve system behavior through prompt iteration and architecture refinement
Pod Collaboration and Delivery* Partner with Software Engineers to integrate AI capabilities into applications, APIs, and user interfaces
- Align with the Lead Engineer on technical direction, architecture, and implementation decisions
- Work with QA Engineers to define evaluation criteria, testing strategies, and quality thresholds for AI outputs
- Translate product requirements into scalable, production\-ready AI solutions
Evaluation and Quality Optimization* Define and implement approaches for evaluating non\-deterministic AI outputs
- Build test cases, benchmarks, and evaluation pipelines to track output quality over time
- Identify failure modes and iterate on prompts, pipelines, and orchestration logic
- Ensure consistency and reliability as models, prompts, and data sources evolve
Continuous Improvement and Innovation* Stay current with advancements in LLMs, vector databases, and agent frameworks
- Experiment with new tools and techniques to improve speed, quality, and capability
- Contribute reusable patterns, components, and best practices across pods
### Skills, Knowledge and Expertise
- 4\+ years of experience in software engineering, applied AI, or machine learning development
- Strong programming skills in Python and/or JavaScript
- Hands\-on experience working with LLM APIs such as Anthropic Claude, OpenAI, or similar
- Experience designing and implementing prompt architectures and prompt engineering techniques
- Experience building retrieval\-augmented generation pipelines and working with vector databases
- Familiarity with agent orchestration frameworks and multi\-step AI workflows
- Experience integrating AI capabilities into applications via APIs and backend systems
- Strong understanding of handling structured and unstructured data in AI systems
- Ability to evaluate, debug, and improve non\-deterministic AI outputs
- Experience working in a fast\-paced, product\-oriented development environment
- Strong problem\-solving skills and ability to operate in ambiguous, evolving contexts
- Ability to collaborate closely with engineers, product managers, and QA within a pod structure
- Excellent communication skills and ability to explain technical concepts clearly
- Curiosity and willingness to learn domain\-specific workflows, particularly within engineering and AEC contexts
### 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.### Our hiring process
Stage 1: Applied
Stage 2: Initial Screening
Stage 3: 1st Interview
Stage 4: Offer
Stage 5: Hired
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Trilon Group AI Hiring
Trilon Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Orlando, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
Frequently Asked Questions
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