Lead Applied AI Engineer/Scientist

RNGD•Metairie, LA

18h

About The Position

RNGD is hiring a Lead Applied AI Engineer/Scientist to lead the technical design, development, evaluation, and continuous improvement of applied AI systems for construction and business problems. This hands-on technical leadership role is accountable for whether AI agents and machine-learning models meet defined business targets, and encompasses architecture, experimentation, evaluation standards, and cross-functional influence. The role spans document AI, computer vision, large language models, retrieval, tool-enabled agents, structured outputs, deterministic software, and human-review workflows. The successful candidate will partner with estimators, domain experts, business leaders, data professionals, and software engineers to translate real-world workflows into reliable AI products. Solutions should be measurable, explainable, production-ready, reviewed at appropriate points, and improved using actual business outcomes. RNGD is a rapidly scaling construction organization headquartered in New Orleans, Louisiana, with additional offices in Mississippi, Tennessee, and Alabama. It describes its culture as focused on innovation, creativity, and building communities, teams, leaders, and the business throughout the Southeast.

Requirements

Three or more years of experience building applied AI or machine-learning systems delivered to real users, including 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, evaluation pipelines, error-analysis processes, data-labeling programs, and ground-truth standards.
Strong Python capabilities.
Experience collaborating on production software, APIs, data pipelines, and cloud-based AI or machine-learning infrastructure.
Ability to explain technical performance, limitations, and recommendations clearly to non-technical stakeholders.

Nice To Haves

Bachelor’s or advanced degree in computer science, machine learning, data science, engineering, or a related field; equivalent relevant experience may be considered.
Experience in construction, engineering, architecture, or manufacturing, particularly involving drawings, estimating workflows, vector PDF parsing, geometric reasoning, vision-language models, or historical cost data.

Responsibilities

Partner with business and domain stakeholders to define AI use cases, 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 information needs and authoritative sources at each workflow stage, and establish processes for missing, conflicting, or ambiguous information.
Identify input-quality issues, workflow dependencies, and technical constraints that may affect performance or reliability.
Design data-labeling and validation approaches with domain experts to establish reliable ground truth.
Maintain traceability from source information through intermediate outputs and model decisions to final business results.
Architect end-to-end AI agents and machine-learning workflows integrating document understanding, computer vision, large language models, retrieval, tool use, business rules, and deterministic code.
Select technical methods for workflow stages based on accuracy, explainability, maintainability, speed, cost, and business risk.
Define structured outputs, data contracts, tool interfaces, orchestration logic, and failure-handling requirements.
Design efficient human-review and approval points, focusing expert attention on high-risk decisions.
Evaluate build-versus-buy options for models, tools, platforms, and supporting infrastructure; conduct feasibility experiments and communicate findings.
Develop and refine models, prompts, retrieval methods, tools, orchestration logic, and supporting data pipelines.
Build benchmark datasets and evaluation pipelines to measure workflow stages and end-to-end business performance.
Define metrics, quality thresholds, test cases, and release criteria based on business requirements.
Conduct error analysis to identify failure sources across inputs, models, prompts, orchestration, tools, and processes.
Prioritize improvements by business impact, frequency, severity, and implementation effort.
Maintain reproducible experiments, evaluation records, model versions, and technical documentation.
Run side-by-side pilots on live projects, comparing AI-generated outputs with human work and verified business outcomes.
Analyze pilot results and explain accuracy, limitations, risks, and improvement priorities in business terms.
Partner with software and data engineers to turn 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 project results and user feedback to improve systems.
Set technical standards for experimentation, evaluation, documentation, model release, monitoring, and continuous improvement.
Provide technical guidance, design reviews, and constructive feedback to engineers, data professionals, vendors, and other contributors.
Recommend architectures, models, platforms, vendors, and technical investments.
Set expectations for data handling, access, traceability, human oversight, and responsible AI use; assess emerging AI capabilities for relevance to RNGD.
Balance innovation with production reliability, maintainability, cost, and organizational readiness.
Work with estimators and domain experts to understand workflows, exceptions, review practices, and quality expectations.
Translate business needs into technical requirements and technical performance into practical business implications.
Communicate results, tradeoffs, risks, dependencies, and limitations to technical and non-technical stakeholders, including when a solution is not reliable enough for production.
Gather user feedback, identify adoption barriers, and incorporate workflow needs into product development.
Align technical priorities with business impact and maintain realistic expectations about AI capabilities and delivery timelines.