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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied AI Solutions Specialist - **Company:** Mott MacDonald - **Location:** New York, NY, United States - **Salary:** $137,000 - $168,000 - **Contract:** Permanent contract - **Skills:** 3d Models, Computer-Aided Design, Geographic Information Systems, AutoCAD, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, ArcGIS (Software), Computer Vision, Autodesk Revit, Cloud Computing, Cyber Security, Information Systems, Databases, Custom Software, Data Governance, Data Infrastructure, Document Management Systems, Information Retrieval, Python (Programming Language), Machine Learning, ProjectWise, Rapid Prototyping Process, Search Technologies, Microsoft SharePoint, Software Engineering, Spatial Data Infrastructures, SQL Databases, Web Application Frameworks, Office365, Retrieval-Augmented Generation, Large Language Models, Generative AI, Microsoft Copilot Studio, AI Platforms, Information Technology, Data Management, Virtual Agents, Autodesk Autocad, Lidar, Engineering Base - **Published:** November 7, 2026 - **Apply:** https://diversityjobs.com/career/18857996/Applied-Ai-Solutions-Specialist-New-York-New-York ## About the Role The ideal candidate combines a strong AEC background with hands-on Applied AI and software development capability, and can independently turn complex project problems into practical, working solutions., * Bachelor's degree in Engineering, Construction Management, Architecture, Computer Science, Artificial Intelligence, Data Science, Information Systems or a related field. * Relevant postgraduate qualifications or professional certifications in engineering, AI, machine learning, software development or digital delivery are a plus. Experience and Skills Required: * Experience in architecture, engineering, construction or major infrastructure delivery, with a practical understanding of how multidisciplinary projects are designed and delivered. * Strong understanding of design and/or construction workflows, including how teams develop, coordinate, review, approve, issue and use project information. * Practical knowledge of AEC systems, including CAD/BIM, Common Data Environments, document management, GIS/spatial and other engineering or project information systems. * Experience with AEC information formats, including drawings, BIM/CAD models, specifications, requirements, calculations, schedules, documents, spatial data, imagery and structured project data. * Strong hands-on experience building AI solutions using Python, AI APIs, SDKs and modern frameworks, including agentic, multi-step and tool-enabled workflows. * Experience applying Generative AI and LLMs to real technical or engineering problems, including retrieval and grounding, structured outputs, tool calling and multimodal AI. * Strong rapid-prototyping capability, moving independently from an ambiguous problem to a working proof of concept, and investigating unfamiliar engineering problems, formats, systems and technologies. * Ability to integrate AI with project information, APIs, automation and existing applications into end-to-end solutions, rather than treating AI as standalone. * Ability to select the right approach, from enterprise and configurable AI to custom development, and recognise when AI is not the right solution. * Understanding of AI evaluation (accuracy, reliability, false positives and negatives, traceability, limitations) and the role of human and engineering validation. * Proven ability to work with project and discipline teams to understand workflows, co-develop solutions, incorporate feedback and support adoption of AI-enabled ways of working. * Ability to translate between AEC practitioners and technical teams, keeping solutions grounded in project needs while viable and scalable. * Clear communication of technical concepts, limitations and findings to technical and non-technical stakeholders, with strong curiosity and ability to learn new technologies and engineering domains quickly. Preferred: * Experience applying AI, machine learning or automation directly within engineering design, construction or infrastructure projects. * Experience prototyping use cases such as design checking, drawing review, requirements assessment, specification analysis, engineering information retrieval, construction information analysis or automated quality checking. * Computer vision or multimodal AI applied to drawings, models, imagery, point clouds or video. * RAG, knowledge-retrieval, AI agent or tool-enabled solutions grounded in controlled project information. * Familiarity with Microsoft 365 Copilot, Copilot Studio and Azure AI Foundry, and which problems suit enterprise, configurable or custom AI. * Autodesk Construction Cloud, Civil 3D, Revit, ProjectWise, ArcGIS or SharePoint. * SQL, databases, vector search and data platforms supporting AI applications. * Transitioning prototypes to solution delivery teams for productionisation and cross-project scaling. * Responsible AI, information security, data governance and engineering assurance considerations. Bring your expertise, curiosity, and drive to make a difference. If you are ready to join a collaborative consultancy where your perspective is valued, your growth is supported, and you are empowered to lead with integrity, we would love to hear from you. Applicants must be authorized to work for any employer in the US, as Mott MacDonald is unable to provide sponsorship at this time. ## Description * Identifying Opportunities: Work with engineering, design and construction teams to understand current workflows, delivery challenges and repetitive or information-intensive activities, and define where AI can improve quality, efficiency, consistency or access to information. * Evaluating Solution Approaches: Assess whether AI is appropriate and select the simplest viable approach: existing capabilities such as Microsoft 365 Copilot, configurable solutions such as Copilot Studio, automation and engineering applications, or custom AI using platforms such as Azure AI Foundry. Avoid unnecessary custom development. * Transforming AEC Workflows: Redesign existing project and discipline processes as AI-enabled workflows, combining AI, automation, engineering technologies, project information and human expertise. * Rapid Prototyping with Projects: Build, test and iterate solutions with the engineers, designers and construction professionals who will use them, moving from an ambiguous problem to a working prototype that shows technical feasibility and project value. * Working Across AEC Information: Work with drawings, BIM/CAD models, specifications, requirements, calculations, GIS and spatial data, imagery, schedules and structured project data. * Applying Appropriate Technologies: Determine where AI adds value and where deterministic logic, automation, APIs, conventional development or existing engineering applications fit better, combining them into end-to-end workflows. * Making Solutions Work and Driving Adoption: Adapt prototypes to project-specific information, standards, systems and practices, then work with project and discipline teams to embed them in day-to-day delivery through demonstrations, user testing, feedback and workflow refinement, resolving barriers to adoption. * Validating with AEC Specialists: Validate AI outputs with engineers and subject-matter experts, understand limitations and establish traceability, assurance and human review before solutions are relied upon. * Transitioning and Scaling Solutions: Transition proven solutions to the Solution Delivery team, providing prototype components, technical knowledge, requirements, user feedback, project context and lessons learned, and help adapt them for other projects with different standards, workflows, information structures, systems and client requirements. * Driving Continuous Improvement: Capture reusable technical approaches, workflow patterns, prototype components and lessons learned, and evaluate emerging AI technologies for AEC delivery.