Lead MLOps Engineer

Description

The AI for Engineering team builds AI software that changes how infrastructure and buildings are designed and engineered, cutting waste and CO₂ and making the built environment more efficient and sustainable.

The team is small and highly technical. It works with start-up speed and ownership while drawing on the scale, resources and domain expertise of a major global engineering firm.

The company is an infrastructure consultancy with over 50,000 planners, engineers, scientists and digital specialists across water, environment, energy, transportation and buildings. It is a Fortune 500 firm that reported $16.1 billion in revenue for fiscal year 2025.

Responsibilities

• Set the MLOps technical direction and platform roadmap for AI for Engineering, along with the engineering standards behind it.
• Lead through design reviews, code reviews, pairing and knowledge sharing, and help engineers grow.
• Design and run production ML infrastructure for training, deployment, serving and monitoring at scale.
• Define the CI/CD, automation, model delivery and ML lifecycle standards that every team follows.
• Write hands-on Python services, APIs and platform tooling for production ML workloads.
• Own reliability, observability, performance, availability, security and cost for production ML systems, including SLOs, incident response and on-call.
• Lead the response to complex production issues across software, infrastructure and ML workflows, and turn what you learn into lasting platform improvements.
• Make key architecture and build-versus-buy decisions, and align ML engineers, data engineers, product teams and leadership on priorities and trade-offs.
• Shape engineering culture, best practices and technical hiring as the team grows.

Requirements

• A bachelor's or master's degree in computer science, engineering or a related field, or equivalent experience.
• Significant experience deploying, operating, monitoring and supporting ML systems in production, including owning an ML platform or a major MLOps capability.
• A proven record as a technical lead or senior/staff engineer, setting direction and influencing others without formal authority.
• Deep experience designing and owning ML pipelines, model deployment, serving, monitoring and lifecycle infrastructure.
• Strong Python skills and experience with a web framework such as FastAPI, Flask or Django.
• Extensive hands-on work with Docker, CI/CD pipelines and cloud environments. The company uses Azure.
• A history of making architecture decisions and running complex production systems for reliability, scalability and performance.
• Experience setting engineering standards, best practices and operational processes that other teams adopt.
• Excellent communication and stakeholder management, 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: Kubernetes and infrastructure as code, such as Terraform.
• Preferred: ML lifecycle tooling such as MLflow, Weights & Biases or similar platforms.
• Preferred: monitoring and observability tools such as Prometheus, Grafana, ELK or OpenTelemetry.
• Preferred: distributed ML workloads, model serving, GPU infrastructure, performance tuning and cloud cost optimisation.
• Preferred: LLMOps and serving generative AI models in production.
• Preferred: background in reinforcement learning, optimisation or generative AI.
• Preferred: experience building an MLOps platform or capability from an early stage.

Conditions

All new hires must attend an in-person Day 1 onboarding at a company office as a condition of employment.

Applicants with a disability or long-term condition who meet the essential criteria for the role are offered an interview under the Disability Confident scheme.

All applicant information is kept confidential in line with EEO guidelines.

Benefits

• Work on real-world problems where AI creates measurable impact.
• See your ideas turn into working systems, on a team where your work matters.
• Collaborate with sharp, driven colleagues in a culture of trust, ownership and high standards.
• Help make the built environment smarter and more sustainable.
• Training and development programs to build technical and leadership skills.
• Benefits, depending on employment status, may include medical, dental, vision, life, AD&D and disability cover, paid time off, leaves of absence, voluntary benefits, perks, flexible work options, well-being resources, an employee assistance program, business travel insurance, service recognition awards, a retirement savings plan and an employee stock purchase plan.