Lead ML Engineer

Are you ready to define how machine‑learning systems are delivered, operated, and scaled across a data‑driven organisation? NewDay are looking for a Lead ML Engineer to set the standards, patterns, and technical foundations that define how machine learning models are delivered and operated in production. 

Why this role matters 

As the technical lead within our MLOps function, you will shape how ML is engineered in practice. This role will focus on establishing the standards, patterns, and technical approaches that enable models to move reliably from experimentation into robust production. 

You will shape how ML systems are engineered in practice through clear lifecycle standards, reusable approaches, and strong technical foundations. 

How you'll contribute 

• Establish standard, reusable patterns for model serving, pipelines, and deployment. 

• Define standards for model packaging, versioning, CI/CD, monitoring and observability. 

• Set clear production readiness criteria for models entering engineering workflows. 

• Provide technical direction for ML solutions delivered within the team. 

• Evolve platform capabilities that reduce bespoke approaches over time. 

• Reduce engineering effort required to productionise models by establishing reusable approaches and platform capabilities. 

We’re looking for these essential skills 

• Experience building and operating ML systems in production, including model serving, monitoring, and lifecycle management. 

• Strong understanding of serving patterns across batch and real‑time environments, and how models move from training to production. 

• Experience defining engineering approaches that enable Data Science teams to deliver reliable, production‑ready models. 

• Ability to turn ambiguity into clear, adoptable technical standards and reusable patterns. 

• Experience setting technical direction and influencing engineering practices across teams. 

• Hands‑on experience with cloud ML platforms (e.g., AWS SageMaker) and orchestration tools (e.g., Airflow, Step Functions) 

• Strong communication skills and the ability to collaborate across Data Science, Engineering, and Data Platforms. 

How you work 

• You are comfortable operating at the boundary between experimentation and production‑grade ML delivery. 

• You value structure, consistency, and high standards in engineering practices. 

• You think in terms of reusable frameworks rather than one‑off solutions. 

• You are motivated by improving reliability and long‑term maintainability of ML systems. 

• You bring clarity, direction, and technical leadership to complex problem spaces. 

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