Lead Applied AI Engineer/Scientist

About the Company

RNGD is a rapidly scaling organization emerging as a nationally recognized leader in construction through our unique Renegade mindset focused on innovation, creativity, and culture. The team is made up of a highly engaged group of seasoned professionals and emerging leaders. We are creators who believe that what we build drives the growth of our cities and that to fulfill our mission, we must constantly challenge the old way of doing things. Headquartered in New Orleans, Louisiana, we thrive on building communities, building teams, building leaders, and growing the business throughout the Southeast region with additional offices in Mississippi, Tennessee, and Alabama.

About the Role

We’re looking for a Lead Applied AI Engineer/Scientist that will lead the technical design, development, evaluation, and continuous improvement of applied AI systems that solve high-value construction and business problems.

This role is accountable for whether AI agents and machine-learning models achieve defined business targets, not simply whether the technology runs. The Lead Applied AI Engineer/Scientist designs the complete workflow, evaluates performance at each stage, identifies where errors originate, and continuously improves the system until the final output meets established business requirements.

This is a hands-on technical leadership role that sets direction through architecture, experimentation, evaluation standards, and cross-functional influence. The role works across document AI, computer vision, large language models, retrieval, tool-enabled agents, structured outputs, deterministic software, and human-review workflows.

The Lead Applied AI Engineer/Scientist partners with estimators, domain experts, business leaders, data professionals, and software engineers to translate real-world workflows into reliable AI products. Success in this role means AI solutions are measurable, explainable, production-ready, reviewed at the appropriate points, and continuously improved using actual business outcomes.

Core Responsibilities

Business Problem Definition, Data, and Input Readiness

Partner with business and domain stakeholders to define use cases, expected outcomes, acceptance criteria, and measurable performance targets.

Audit real-world inputs, including spreadsheets, vector and scanned PDFs, two-dimensional drawings, scales, symbol conventions, revisions, specifications, images, and three-dimensional models.

Define the information required at each workflow stage, identify authoritative data sources, and establish how missing, conflicting, or ambiguous information should be handled.

Identify input-quality issues, workflow dependencies, and technical constraints that may affect model performance or business reliability.

Design data-labeling and validation approaches with domain experts to establish reliable ground truth.

Maintain traceability between source information, intermediate outputs, model decisions, and final business results.

AI Architecture and Workflow Design

Architect end-to-end AI agents and machine-learning workflows that connect document understanding, computer vision, large language models, retrieval, tool use, business rules, and deterministic code.

Select the appropriate technical method for each workflow stage based on accuracy, explainability, maintainability, speed, cost, and business risk.

Define structured outputs, data contracts, tool interfaces, orchestration logic, and failure-handling requirements.

Design human-review and approval points that allow domain experts to verify outputs efficiently and focus attention on the highest-risk decisions.

Evaluate build-versus-buy options for models, tools, platforms, and supporting infrastructure.

Conduct feasibility experiments before major technical or financial commitments and communicate findings clearly to business and technical leaders.

Model Development, Evaluation, and Error Analysis

Develop and refine models, prompts, retrieval methods, tools, orchestration logic, and supporting data pipelines.

Build benchmark datasets and evaluation pipelines that measure individual workflow stages and end-to-end business performance.

Define meaningful metrics, quality thresholds, test cases, and release criteria based on business requirements.

Perform detailed error analysis to identify where failures originate and distinguish input, model, prompt, orchestration, tool, and process issues.

Prioritize improvements based on business impact, frequency, severity, and implementation effort.

Maintain reproducible experiments, evaluation records, model versions, and technical documentation to support reliable decision-making.

Pilot Delivery, Productionization, and Monitoring

Run side-by-side pilots using live projects and compare AI-generated outputs with human work and verified business outcomes.

Analyze pilot results and explain accuracy, limitations, risks, and improvement priorities in clear business terms.

Partner with software and data engineers to convert prototypes into maintainable production code, APIs, workflows, and cloud-based infrastructure.

Establish production-readiness requirements, monitoring controls, fallback procedures, and appropriate human-review mechanisms.

Monitor production accuracy, failure patterns, user corrections, and changing data conditions.

Use actual project results and user feedback to improve future model and workflow performance.

Technical Leadership, Standards, and Responsible AI

Set technical direction and standards for experimentation, evaluation, documentation, model release, monitoring, and continuous improvement.

Provide technical guidance, design review, and constructive feedback to engineers, data professionals, vendors, and other contributors supporting AI initiatives.

Make informed recommendations regarding architecture, models, platforms, vendors, and technical investments.

Establish appropriate expectations for data handling, access, traceability, human oversight, and responsible AI use.

Maintain awareness of emerging AI capabilities and assess their relevance to RNGD’s workflows and business priorities.

Balance innovation with production reliability, maintainability, cost, and organizational readiness.

Cross-Functional Partnership and Business Communication

Work closely with estimators and other domain experts to understand real workflows, exceptions, review practices, and quality expectations.

Translate business needs into clear technical requirements and technical performance into practical business implications.

Communicate model results, tradeoffs, risks, dependencies, and limitations clearly to technical and non-technical stakeholders.

State candidly when a solution is not yet reliable enough for production use and explain what must improve.

Gather user feedback, identify adoption barriers, and incorporate practical workflow needs into product development.

Align technical priorities with business impact while maintaining realistic expectations regarding AI capabilities and delivery timelines.

Preferred Qualifications

Bachelor’s or advanced degree in computer science, machine learning, data science, engineering, or a related field preferred; equivalent relevant experience may be considered.

Three or more years of experience building applied AI or machine-learning systems delivered to real users, including experience providing technical direction for an AI product.

Hands-on experience with document AI, computer vision, or complex-document processing, including layout analysis, optical character recognition, object or symbol detection, or measurement from images.

Experience building large-language-model agents or multi-step AI workflows involving tool use, orchestration, structured outputs, retrieval, and deterministic components.

Demonstrated experience developing benchmark datasets, evaluat