Tech Lead AWS in London

At AECOM, we're delivering a better world. Whether improving your commute, keeping the lights on, providing access to clean water, or transforming skylines, our work helps people and communities thrive. We are the world's trusted infrastructure consulting firm, partnering with clients to solve the world's most complex challenges and build legacies for future generations.

With accelerating infrastructure investment worldwide, our services are in great demand. We invite you to bring your bold ideas and big dreams and become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and other professionals delivering projects that create a positive and tangible impact around the world.

In AECOM's AI for 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.

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. 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.

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 optimisation.
• Experience with LLMOps and serving generative AI models in production.
• Background in reinforcement learning, optimisation or generative AI.
• Experience building an MLOps platform or capability from an early stage.

Additional Information

To support this commitment, all newly hired employees are required to attend an in-person Day 1 onboarding at an AECOM office location as a condition of employment. Depending on your employment status, AECOM benefits may include medical, dental, vision, life, AD&D, disability benefits, paid time off, leaves of absences, voluntary benefits, perks, flexible work options, well-being resources, employee assistance program, business travel insurance, service recognition awards, retirement savings plan, and employee stock purchase plan.

We are a Disability Confident Employer and will offer an interview to applicants who have a disability or long-term condition, who meet the minimum/essential criteria for the role.