Senior ML Engineer

Harnham - Data and Analytics Recruitment; City Of London; Hybride; GBP 75 000 - 85 000; Plein temps

Avantages offerts par ce poste

Ongoing training and development

Ownership of ML Ops function

Résumé du poste

Harnham - Data and Analytics Recruitment is seeking a Senior ML Ops Engineer in London with hybrid work arrangement. You will own and evolve infrastructure for AI/ML services, deploy and monitor production models, and build Kubernetes-based, cloud-native pipelines.

You will collaborate with Data Scientists, software engineers and client teams to deliver scalable AI solutions, applying IaC (Terraform/Bicep) and CI/CD practices to ensure reliability and security.

Qualifications

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

Responsabilités

• Own and evolve the infrastructure powering AI/ML services.
• Design and maintain production ML orchestration pipelines.
• Deploy, monitor, and scale ML models in production.
• Build cloud-native infra with Kubernetes and IaC.
• Develop robust CI/CD and observability for ML ops.
• Collaborate with Data Scientists and engineers to deliver scalable AI solutions.
• Influence standards and future direction of the AI platform.

Connaissances

Python

ML infrastructure

Orchestration tools

Kubernetes

Infrastructure as Code

CI/CD pipelines

Cloud platforms (Azure)

LLM/NLP exposure

Production deployment

Outils

Dagster

Airflow

Prefect

Terraform

Bicep

Pulumi

Description du poste

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.