Senior ML Engineer

Artificial Intelligence Jobs; Greater London; Hybrid; GBP 75,000 - 85,000; Full time

Benefits offered by this job

Annual bonus (10%)

Job summary

Artificial Intelligence Jobs in London is seeking a Senior ML Ops Engineer to own the infrastructure, deployment, and operational excellence of production ML systems in a hybrid setup (1-2 days per week). You will design production-grade ML orchestration pipelines, deploy and monitor models, build cloud-native infra with Kubernetes, and drive CI/CD and observability to scale AI services.

You will work with Dagster, Airflow, or Prefect, use Terraform or Bicep, and collaborate with data scientists

Qualifications

• Strong Python software engineering experience.
• Experience deploying ML infrastructure in production.
• Knowledge of orchestration tools (Dagster/Airflow/Prefect).
• Hands-on with Kubernetes and containerised workloads.
• IaC experience (Terraform/Bicep/Pulumi).
• Experience building CI/CD pipelines and monitoring/observability.
• Cloud experience, ideally Azure.
• Exposure to LLMs, NLP, or generative AI.

Responsibilities

• Own and evolve infrastructure powering AI/ML services.
• Design, build, and maintain production-grade ML orchestration pipelines (Dagster, Airflow, Prefect).
• Deploy, monitor, and scale ML models and LLM-powered solutions.
• Build cloud-native infrastructure with Kubernetes and IaC (Terraform/Bicep).
• Develop robust CI/CD and observability frameworks for ML operations.
• Collaborate with Data Scientists, software engineers, and client teams to deliver scalable AI solutions.
• Influence technical standards and future direction of the AI platform.

Skills

Python

ML Ops

Kubernetes

CI/CD

Observability

Azure

LLMs

IaC

Tools

Dagster

Airflow

Prefect

Terraform

Bicep

Pulumi

Job description

Senior ML Ops Engineer

London (Hybrid, 1-2 days per week) | £75,000 - £85,000 + 10% Bonus

This is an opportunity to join a growing AI and data organisation that is using advanced analytics and machine learning to help public sector organisations make better, more informed decisions. You will play a pivotal role as the ML Ops specialist within the AI function, owning the infrastructure, deployment, and operational excellence of production machine learning systems.

The Company

They are an established data and AI business focused on developing innovative machine learning and analytics solutions that deliver meaningful real-world impact. Their platform combines predictive modelling, text analytics, and emerging AI capabilities to support complex decision-making at scale. As the organisation continues to grow, they are expanding their AI engineering capability and investing heavily in their platform and infrastructure.

The Role

You will:

• Own and evolve the infrastructure that powers a suite of AI and machine learning services.
• Design, build, and maintain production-grade ML orchestration pipelines using tools such as Dagster, Airflow, Prefect, or similar technologies.
• Deploy, monitor, and scale machine learning models and LLM-powered solutions in production environments.
• Build and maintain cloud-native infrastructure using Kubernetes and Infrastructure as Code technologies such as Terraform or Bicep.
• Develop robust CI/CD processes and observability frameworks to ensure reliable and secure ML operations.
• Collaborate closely with Data Scientists, software engineers, and client-facing teams to deliver scalable AI solutions.
• Influence technical standards, best practices, and the future direction of the organisation's AI platform.

Your Skills & Experience

You will have:

• Strong commercial experience in Python software engineering.
• Experience deploying and managing machine learning infrastructure in production.
• Knowledge of orchestration tools such as Dagster, Airflow, Prefect, or similar.
• Hands‑on experience with Kubernetes and containerised workloads.
• Expertise in Infrastructure as Code using Terraform, Bicep, Pulumi, or comparable technologies.
• Experience building CI/CD pipelines and implementing monitoring and observability practices.
• Experience working with cloud platforms, ideally Azure, although other cloud backgrounds will be considered.
• Exposure to LLMs, NLP, text analytics, or generative AI applications.
• The ability to deploy infrastructure that supports scalable, reliable machine learning services.

What They Offer

• Ongoing training and development opportunities.
• The chance to take ownership of a critical ML Ops function and influence the future of a growing AI platform.