Full job descriptionApplied Ai EngineerAECOM is seeking an Applied AI Engineer to join our Applied AI team, contributing to transformative AI-enabled delivery across our clients’ projects. As part of AECOM’s commitment to delivering sustainable and innovative solutions, the Applied AI Engineer will play a critical role in designing, building and scaling practical AI solutions that unlock measurable business value across infrastructure, environmental, and urban development sectors.
Reporting to the Applied AI Lead, this role will focus on building agentic workflows, integrating AI with client data and systems, and turning proven solutions into repeatable offerings for many different clients. The ideal candidate will bring expertise in large language models, software engineering and data integration, with a passion for solving complex challenges in the built environment through simple, effective and well-tested solutions.
Key responsibilities include designing, building and deploying AI-enabled solutions that address real client and project problems, deploying and operating AI solutions on Microsoft Azure, developing agentic and multi-step workflows, building and maintaining Model Context Protocol servers and tool interfaces, integrating diverse data sources into reliable, tested pipelines, hosting, configuring and optimizing local and private large language models, building retrieval-augmented generation and document-intelligence pipelines, optimizing token usage, latency and cost, establishing evaluation, testing and monitoring practices for AI solutions, designing data models, dashboards and reports that present AI outputs clearly to project teams and decision-makers, applying security, data protection and responsible AI practices, turning successful solutions into reusable, configurable components, documenting work through specifications, implementation plans and post-delivery reviews, supporting bids and proposals by shaping AI solutions to client requirements, mentoring colleagues and supporting AI training programs, and presenting work outputs to both technical and non-technical audiences.
Minimum requirements include 3–5+ years of experience in software engineering, data engineering or applied machine learning, strong proficiency in Python and SQL, experience developing LLM applications with tool use, structured outputs, retrieval-augmented generation, agent orchestration and prompt design, understanding of Microsoft Azure and experience deploying AI solutions on it, proven ability to influence non-technical stakeholders and communicate complex concepts clearly, and experience mentoring and coaching technical teams.
Preferred qualifications include a degree in Computer Science, Software Engineering, Data Science or a related field, or equivalent practical experience, familiarity with Model Context Protocol, multi-agent frameworks, evaluation harnesses and LLM observability tooling.