Full job descriptionLead MLOps EngineerIn AECOM’s AI for Engineering team your code will directly shape the physical world around us. We build AI-driven technology that revolutionizes how infrastructure and buildings are designed and engineered, reducing waste, cutting CO2, and making the built environment more efficient and sustainable. This is where software has measurable, real-world impact.
Our AI for Engineering team has a unique setup: a lean, highly technical team with the speed and ownership of a start-up, backed by the scale, resources, and domain expertise of one of the world’s leading engineering firms.
There has never been a better time to be at AECOM. We are leading the industry’s AI transformation, and with our people and technology we deliver excellence and innovate with impact.
What You’ll Do
As Lead MLOps Engineer, you will be the technical lead for how machine learning moves from experimentation into reliable, scalable production across AI for Engineering. This is a senior, hands-on role. You will own the MLOps technical direction and platform roadmap, raise the bar for engineering practice across the team, and build the most critical parts of the stack yourself. You will be the go-to technical authority for production ML infrastructure and a trusted partner to engineering leadership.
*Define and drive the MLOps technical vision, platform roadmap and engineering standards across AI for Engineering
*Provide technical leadership and mentoring to engineers through design reviews, code reviews, pairing and knowledge sharing
*Architect and own production ML infrastructure for training, deployment, serving and monitoring at scale
*Set the standards for CI/CD, automation, model delivery and ML lifecycle management used across all teams
*Stay hands-on, building Python services, APIs and platform tooling that support production ML workloads
*Own reliability, observability, performance, availability, security and cost across production ML systems, including SLOs, incident response and on-call practices
*Lead the technical response to complex production issues across software, infrastructure and ML workflows, and turn the lessons into lasting platform improvements
*Make and communicate key architecture and build-vs-buy decisions, aligning ML engineers, data engineers, product teams and leadership on priorities and trade-offs
*Help shape engineering culture, best practices and technical hiring as the team scales
QualificationsMust-Have Qualifications
*Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience)
*Significant experience deploying, operating, monitoring and supporting machine learning systems in production, including ownership of an ML platform or major MLOps capability
*Proven experience as a technical lead or in a senior/staff-level role, setting technical direction and influencing engineers without relying on formal authority
*Deep experience designing and owning ML pipelines, model deployment, serving, monitoring and lifecycle infrastructure
*Strong Python skills, plus experience with web frameworks such as FastAPI, Flask or Django
*Extensive hands-on experience with Docker, CI/CD pipelines and cloud environments (we use Azure)
*A track record of making architecture decisions and running complex production systems for reliability, scalability and performance
*Experience defining engineering standards, best practices and operational processes adopted across teams
*Excellent communication and stakeholder management skills, with the ability to influence technical and non-technical audiences
*A collaborative style that brings out the best in others and builds a high-ownership engineering culture
Preferred Skills
*Expertise with Kubernetes and infrastructure as code, such as Terraform
*Experience with ML lifecycle tooling such as MLflow, Weights & Biases or similar platforms
*Knowledge of monitoring and observability tools such as Prometheus, Grafana, ELK or OpenTelemetry
*Experience with distributed ML workloads, model serving, GPU infrastructure, performance tuning and cloud cost optimization
*Experience with LLMOps and serving generative AI models in production
*Background in reinforcement learning, optimization or generative AI
*Experience building an MLOps platform or capability from an early stage
Additional InformationOur Hiring Process
*25-minute screening call
*Combined technical and cultural interview (in-person) *Whiteboard Interview: 1-hour with 2 of our engineers to discuss your solution to the take-home challenge
*Culture fit: 30-minute meeting with our leadership team

Why Join Us?
*Work on real-world problems where AI creates measurable impact.
*Be part of a team where your work matters, and your ideas become real.
*Collaborate with sharp, driven colleagues in a culture of trust, ownership, and high standards.
*Contribute to making the built environment smarter and more sustainable.