Principal AI Engineer at AECOM2

We’re building AI systems that don’t sit in demos; they run the business. This is a Principal-level, hands-on engineering role focused on solving the most complex, high-impact problems and turning them into reliable, production-grade systems. You will operate at the intersection of AI and distributed systems, designing and building solutions that integrate into real workflows across HR, Finance, IT, and Legal. You are accountable for both how systems are designed and how they perform in production, such as reliability, latency, cost, and failure handling. This role goes beyond execution. The systems you build and the decisions you make will influence how similar problems are approached across teams, raising the bar for how AI is applied in production. We’re looking for someone who is deeply hands-on, highly resourceful, and comfortable operating in ambiguity. You connect dots quickly, move from problem to solution without waiting for perfect direction, and take ownership of outcomes, not just code. If you care about impact, autonomy, and seeing your work run at scale in real environments, this is that role. This position will offer flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work, to be based from either Houston or Dallas, TX. Key Responsibilities • Design and build production AI systems end-to-end, from initial concept through deployment and operation • Own the architecture and implementation of the systems you build, ensuring they are scalable, reliable, and maintainable • Translate ambiguous, high-impact problems into practical system designs and working solutions • Drive technical direction through your work, influencing how similar systems are designed and built across teams • Integrate AI capabilities (LLMs, RAG, agents) into real applications, services, and workflows • Design and implement services, APIs, and event-driven systems that connect AI with enterprise platforms • Own system behavior in production, including reliability, latency, cost, and failure handling • Establish strong observability, evaluation, and feedback loops to continuously improve system performance • Debug and resolve complex issues across data, models, infrastructure, and application layers • Raise the bar for engineering quality through hands-on development and technical leadership