Applied AI Field Engineer

Trilon; Charlotte (NC); On-site; USD 155,000 - 190,000; Full time

Job summary

Trilon in Charlotte, NC, is seeking an Applied AI Engineer to design and implement AI-powered features that reduce real engineering labor across our operating companies. You will work at the intersection of software engineering and applied AI, building prompt frameworks, retrieval-augmented generation pipelines, and agent-based workflows that operate on real engineering data.

Collaborate with lead engineers, software engineers, and QA to deliver production-ready AI solutions in a fast-moving,

Qualifications

• 4+ years of software engineering or ML/AI development experience.
• Strong Python/JavaScript programming skills.
• Hands-on with LLM APIs (Anthropic Claude, OpenAI) and prompt engineering.
• Experience building retrieval-augmented generation and vector databases.
• Familiar with agent orchestration frameworks and multi-step AI workflows.

Responsibilities

• Design and build AI-powered features using LLMs and related tooling.
• Develop prompt architectures and retrieval-augmented generation pipelines.
• Integrate AI capabilities into applications via APIs and backend systems.
• Collaborate with pod teams to deliver production-ready AI solutions.
• Evaluate and improve AI outputs for reliability and accuracy.

Skills

Python

JavaScript

LLM APIs

Prompt engineering

Vector databases

Agent orchestration

APIs & backend systems

Tools

Anthropic Claude

OpenAI API

LLM tooling

Job description

Compensation: $155,000 - $190,000 / year

Department: IT

Location: Charlotte, NC

Compensation: $155,000 - $190,000 / year

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