Lead ML Platform Engineer - Production AI

Accelerant; United Kingdom; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Accelerant is seeking a leader to own how machine learning and AI run in production. You will supervise data pipelines, training and inference services, and the platform powering our data scientists.

You will partner with data scientists, engineers and risk teams to ensure reliable, scalable systems with a focus on reproducibility, governance, and cost efficiency across the accelerant platform.

Qualifications

• Production ML systems experience after launch.
• Strong software foundations and cloud experience.
• Data engineering with pipelines and SQL proficiency.
• Ability to influence cross-functional projects.
• Statistical literacy to discuss model performance.
• Experience leading engineers and teams.
• Willingness to work with LLMs and agentic AI.
• Excellent communication with stakeholders.

Responsibilities

• Own ML platform end-to-end: data pipelines, training, and inference.
• Design integrations with wider Accelerant platform and external systems.
• Make deployments routine with CI/CD and versioning for models.
• Build monitoring for data drift, pipeline health, and delays.
• Stand up infrastructure for agentic AI including orchestration and cost control.
• Own reliability, cost, and performance across ML workloads.
• Lead and grow the function, setting standards and coaching staff.

Skills

ML production

Python

Infrastructure as code

Containers & orchestration

SQL

Data integration

LLMs / agentic AI

Engineering leadership

Cost & failure modes

Communication &Stakeholders

Tools

CI/CD

Model registry

Job description

Accelerant is seeking a leader to own how machine learning and AI run in production. You will supervise data pipelines, training and inference services, and the platform powering our data scientists.

You will partner with data scientists, engineers and risk teams to ensure reliable, scalable systems with a focus on reproducibility, governance, and cost efficiency across the accelerant platform.