MLOps Engineer: AI Pipelines & On-Site Infra

AECO; Greater London; On-site; GBP 85,000 - 120,000; Full time

Job summary

AECOM’s AI Engineering team is hiring to shape the next generation of AI driven infrastructure software in a fast‑moving, high‑ownership environment. You will own the delivery pipelines and backend systems that keep AI products reliable, scalable and secure, with real world impact on infrastructure projects.

You will collaborate with ML engineers, data engineers and product teams to deliver end‑to‑end features, optimize performance, and drive MLOps best practices in Azure cloud on‑premise and

Qualifications

• Degree in CS/Engineering or equivalent experience.
• Strong Python coding skills and experience with web libraries (FastAPI, Flask, or Django).
• Hands‑on ML model deployment and monitoring in production.
• Experience with containerization (Docker) and CI/CD.
• Familiarity with Azure cloud environments and deployments.
• Excellent communication and collaboration skills.

Responsibilities

• Own infrastructure and delivery pipelines for AI‑driven products.
• Build and maintain robust ML pipelines for training, deployment, and monitoring.
• Develop backend systems and APIs to integrate AI into our SaaS platform.
• Ensure ML performance, monitoring, availability, and security.
• Collaborate with ML/data engineers and product teams to deliver features end‑to‑end.
• Contribute to scalable, cloud‑native ML infrastructure decisions.

Skills

Python

ML Pipelines

Docker

CI/CD

Azure

Web Frameworks (FastAPI/Flask/Django)

Communication

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Tools

Docker

CI/CD tools

Cloud environments

Job description

AECOM’s AI Engineering team is hiring to shape the next generation of AI driven infrastructure software in a fast‑moving, high‑ownership environment. You will own the delivery pipelines and backend systems that keep AI products reliable, scalable and secure, with real world impact on infrastructure projects.

You will collaborate with ML engineers, data engineers and product teams to deliver end‑to‑end features, optimize performance, and drive MLOps best practices in Azure cloud on‑premise and