{"id":3971151,"url":"https://alion.io/job/rngd-lead-applied-ai-engineerscientist","title":"Lead Applied AI Engineer/Scientist","company":{"id":4421105,"name":"Rngd","domain":"rngd.com","url":"https://alion.io/company/rngd-2","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":{"grade":"B","score":75,"open_postings":12,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-11T05:49:22Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Metairie, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Computer Vision","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Function Calling","optional":false},{"name":"Machine Learning","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-11-09T16:11:35Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-11T09:16:42Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About the Company\nRNGD 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.\nAbout the Role\nWe’re looking for a Lead Applied AI Engineer/Scientistthat will lead the technical design, development, evaluation, and continuous improvement of applied AI systems that solve high-value construction and business problems.\nThis 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.\nThis 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.\nThe 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.\nCore Responsibilities\nBusiness Problem Definition, Data, and Input Readiness\nPartner with business and domain stakeholders to define use cases, expected outcomes, acceptance criteria, and measurable performance targets.\nAudit real-world inputs, including spreadsheets, vector and scanned PDFs, two-dimensional drawings, scales, symbol conventions, revisions, specifications, images, and three-dimensional models.\nDefine the information required at each workflow stage, identify authoritative data sources, and establish how missing, conflicting, or ambiguous information should be handled.\nIdentify input-quality issues, workflow dependencies, and technical constraints that may affect model performance or business reliability.\nDesign data-labeling and validation approaches with domain experts to establish reliable ground truth.\nMaintain traceability between source information, intermediate outputs, model decisions, and final business results.\n\nAI Architecture and Workflow Design\nArchitect 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.\nSelect the appropriate technical method for each workflow stage based on accuracy, explainability, maintainability, speed, cost, and business risk.\nDefine structured outputs, data contracts, tool interfaces, orchestration logic, and failure-handling requirements.\nDesign human-review and approval points that allow domain experts to verify outputs efficiently and focus attention on the highest-risk decisions.\nEvaluate build-versus-buy options for models, tools, platforms, and supporting infrastructure.\nConduct feasibility experiments before major technical or financial commitments and communicate findings clearly to business and technical leaders.\n\nModel Development, Evaluation, and Error Analysis\nDevelop and refine models, prompts, retrieval methods, tools, orchestration logic, and supporting data pipelines.\nBuild benchmark datasets and evaluation pipelines that measure individual workflow stages and end-to-end business performance.\nDefine meaningful metrics, quality thresholds, test cases, and release criteria based on business requirements.\nPerform detailed error analysis to identify where failures originate and distinguish input, model, prompt, orchestration, tool, and process issues.\nPrioritize improvements based on business impact, frequency, severity, and implementation effort.\nMaintain reproducible experiments, evaluation records, model versions, and technical documentation to support reliable decision-making.\n\nPilot Delivery, Productionization, and Monitoring\nRun side-by-side pilots using live projects and compare AI-generated outputs with human work and verified business outcomes.\nAnalyze pilot results and explain accuracy, limitations, risks, and improvement priorities in clear business terms.\nPartner with software and data engineers to convert prototypes into maintainable production code, APIs, workflows, and cloud-based infrastructure.\nEstablish production-readiness requirements, monitoring controls, fallback procedures, and appropriate human-review mechanisms.\nMonitor production accuracy, failure patterns, user corrections, and changing data conditions.\nUse actual project results and user feedback to improve future model and workflow performance.\n\nTechnical Leadership, Standards, and Responsible AI\nSet technical direction and standards for experimentation, evaluation, documentation, model release, monitoring, and continuous improvement.\nProvide technical guidance, design review, and constructive feedback to engineers, data professionals, vendors, and other contributors supporting AI initiatives.\nMake informed recommendations regarding architecture, models, platforms, vendors, and technical investments.\nEstablish appropriate expectations for data handling, access, traceability, human oversight, and responsible AI use.\nMaintain awareness of emerging AI capabilities and assess their relevance to RNGD’s workflows and business priorities.\nBalance innovation with production reliability, maintainability, cost, and organizational readiness.\n\nCross-Functional Partnership and Business Communication\nWork closely with estimators and other domain experts to understand real workflows, exceptions, review practices, and quality expectations.\nTranslate business needs into clear technical requirements and technical performance into practical business implications.\nCommunicate model results, tradeoffs, risks, dependencies, and limitations clearly to technical and non-technical stakeholders.\nState candidly when a solution is not yet reliable enough for production use and explain what must improve.\nGather user feedback, identify adoption barriers, and incorporate practical workflow needs into product development.\nAlign technical priorities with business impact while maintaining realistic expectations regarding AI capabilities and delivery timelines.\n\nPreferred Qualifications\nBachelor’s or advanced degree in computer science, machine learning, data science, engineering, or a related field preferred; equivalent relevant experience may be considered.\nThree 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.\nHands-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.\nExperience building large-language-model agents or multi-step AI workflows involving tool use, orchestration, structured outputs, retrieval, and deterministic components.\nDemonstrated experience developing benchmark datasets, evaluation pipelines, error-analysis processes, data-labeling programs, and ground-truth standards.\nStrong Python capabilities and experience collaborating on production software, APIs, data pipelines, and cloud-based AI or machine-learning infrastructure.\nExperience in construction, engineering, architecture, or manufacturing preferred, particularly involving drawings, estimating workflows, vector PDF parsing, geometric reasoning, vision-language models, or historical cost data.\nAbility to explain technical performance, limitations, and recommendations clearly to non-technical stakeholders.\nCore Competencies\nRNGD Values Alignment\nExemplifies RNGD’s core values:\nRenegade Mentality\nSweep Floors\nBuild Together\nHonor Promises\nApplied AI Architecture and Technical Execution\nDesigns reliable end-to-end AI systems by selecting and integrating the models, tools, data, deterministic components, and human-review points appropriate to the business problem.\nDemonstrates a Renegade Mentality by challenging assumptions, testing emerging approaches, and finding practical ways to solve complex problems.\n\nEvaluation Rigor and Analytical Judgment\nUses benchmarks, stage-level metrics, production results, and structured error analysis to determine whether systems meet defined technical and business expectations.\nDemonstrates the Sweep Floors mindset by examining the details behind performance, maintaining disciplined documentation, and following failures to their actual source.\n\nBusiness Impact and Product Ownership\nConnects technical decisions to measurable business outcomes and prioritizes improvements according to their effect on users, accuracy, risk, cost, and operational value.\nHonors Promises by communicating capabilities honestly, setting realistic expectations, and ensuring released systems perform against agreed standards.\n\nCross-Functional Partnership and Communication\nTranslates effectively between domain experts, business leaders, data professionals, and software engineers while creating shared understanding of requirements and results.\nBuilds Together by incorporating diverse expertise, communicating clearly, and designing solutions with the people responsible for using and supporting them.\n\nResponsible Innovation and Continuous Improvement\nBalances experimentation and speed with traceability, human oversight, production reliability, maintainability, and responsible use.\nCombines a Renegade Mentality with Honor Promises by pursuing better methods without compromising transparency, quality, or 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