Senior ML Ops Engineer 201043

Harnham; United Kingdom; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Ongoing training

Ownership of ML Ops function

Job summary

Harnham is seeking an ML Ops specialist to own the infrastructure powering a suite of AI/ML services in production, collaborating with data scientists and software engineers.

You will design and maintain production-grade orchestration pipelines (Dagster, Airflow, Prefect), build cloud-native infra with Kubernetes and Terraform/Bicep, and drive CI/CD and observability to ensure reliability.

Qualifications

• Strong Python programming experience.
• Experience deploying ML infrastructure in production.
• Knowledge of orchestration tools (Dagster, Airflow, Prefect).
• Hands-on with Kubernetes and container workloads.
• IaC with Terraform, Bicep, Pulumi or similar.
• CI/CD pipelines and observability practices.
• Experience with cloud platforms, ideally Azure.
• Exposure to LLMs, NLP or generative AI.
• Deploy infrastructure supporting scalable ML services.

Responsibilities

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

Skills

Python programming

Production ML deployment

Cloud experience

Collaboration

Technical standards

Tools

Dagster

Airflow

Prefect

Kubernetes

Terraform

Bicep

Pulumi

Azure

LLMs exposure

Job description

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.