MLOps Engineer

In AECOM’s AI Engineering team your code will directly shape the physical world around us. We build AI-driven technology that revolutionises 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. With our AI Engineering team we’ve created 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. We invite you to bring your bold ideas and big dreams to solve the world’s most complex challenges. We're one global team driven by our common purpose to deliver a better world. Join us. What You’ll Do As part of our AI Engineering team, you will own the infrastructure and delivery pipelines that keep our AI-driven products reliable, scalable, and fast. This is a high-ownership role where your decisions directly affect developer velocity and production stability. - Build and maintain robust ML pipelines for training, deployment, and monitoring - Develop backend systems and APIs that integrate AI into our SaaS platform - Take ownership of the ML performance, monitoring, availability, and security - Collaborate with ML engineers, data engineers, and product teams to deliver features end-to-end - Contribute to architecture decisions for scalable, cloud-native ML infrastructure - Stay up to date with the latest in MLOps practices and tools, and bring improvements into the workflow Must-Have Qualifications - Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience) - Solid programming skills in Python (plus experience with web libraries like FastAPI, Flask, or Django) - Hands-on experience with ML model deployment and monitoring in production Knowledge of containerization (Docker) - Experience with CI/CD pipelines and cloud environments (we use Azure) - Strong communication skills and a collaborative mindset Preferred Skills - Experience with model versioning and experiment tracking (e.g. MLflow, Weights & Biases) - Understanding of optimizing both CPU-bound and GPU-bound workloads - Knowledge of monitoring and observability tools (e.g. Prometheus, Grafana, ELK stack) - Background in optimization, reinforcement learning, or generative AI (a plus, not required) - Identifying and resolving bottlenecks in distributed machine learning workloads (knowledge of low-level languages and CUDA library is a plus)