Senior MLOps Engineer 201043

Harnham - Data and Analytics Recruitment; Greater London; Hybrid; GBP 51,000 - 85,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Harnham is seeking a Senior MLOps Engineer in London (Hybrid) to scale and stabilise our ML platforms. You will build and evolve cloud infrastructure, deploy ML models with Azure ML and AKS, and design robust pipelines for training, deployment and retraining.

Your work will emphasize reliability, security, and governance across client environments. You will collaborate with data scientists and engineers to implement IaC, monitoring, and observability using Prometheus, Grafana, and related tools,

Qualifications

• Strong experience in MLOps/Platform Engineering for ML systems.
• Advanced Python programming with a software engineering mindset.
• Hands-on experience with Azure-native MLOps services, AKS, and model deployment pipelines.

Responsibilities

• Build, maintain, and evolve cloud infrastructure for ML/AI services in production.
• Deploy, manage, and monitor ML models using Azure ML and AKS.
• Design orchestration pipelines for training, deployment, inference, and retraining workflows.
• Implement infrastructure-as-code with Terraform, Bicep, or Pulumi.

Skills

MLOps

Platform Engineering

Python

Azure MLOps

Kubernetes

CI/CD

Terraform/Bicep

Observability

Dagster/Airflow/Prefect

Azure DevOps

Tools

Azure ML

AKS (Azure Kubernetes Service)

Terraform

Bicep

Pulumi

Dagster

Airflow

Prefect

Prometheus

Grafana

Azure Monitor

Job description

Senior MLOps Engineer

London (Hybrid) | Salary: up to £85,000

This is an opportunity to join a growing AI and data organisation where your work will directly support the deployment, scalability, and reliability of innovative machine learning solutions. You will play a pivotal role in building the infrastructure that enables advanced AI services to deliver real-world impact at scale.

The Company

They are a mission-led technology organisation that uses data, machine learning, and AI to help organisations make better decisions and deliver meaningful outcomes. With a growing client base and continued investment in their platform, they are expanding their engineering capabilities to support the next stage of growth. Their environment combines technical excellence with a strong focus on responsible AI, innovation, and continuous improvement. You will be joining a collaborative team where your expertise will help shape both the platform and the wider AI strategy.

The Role

• Build, maintain, and evolve the cloud infrastructure that supports machine learning and AI services in production.
• Deploy, manage, and monitor machine learning models using Azure ML, Azure Kubernetes Service (AKS), and related Azure services.
• Design and develop robust orchestration pipelines for model training, deployment, inference, and retraining workflows.
• Implement infrastructure-as-code solutions using tools such as Terraform, Bicep, or similar technologies.
• Establish monitoring, logging, alerting, and observability frameworks to ensure reliability and performance.
• Support scalable deployment across multiple client environments while maintaining security and operational excellence.
• Collaborate with data scientists, engineers, and wider business stakeholders to enable successful AI delivery.
• Contribute to AI governance, best practices, and responsible AI principles across the organisation.

Your Skills & Experience

• Strong commercial experience within MLOps, Platform Engineering, or Infrastructure Engineering supporting machine learning systems.
• Advanced Python programming skills with a strong software engineering mindset.
• Hands‑on experience with Azure-native MLOps services, including model deployment, pipelines, environments, and compute resources.
• Proven expertise deploying and managing containerised applications on Kubernetes.
• Experience building and maintaining CI/CD pipelines using Azure DevOps or similar tools.
• Knowledge of infrastructure-as-code approaches using Terraform, Bicep, Pulumi, or equivalent technologies.
• Experience implementing monitoring and observability solutions using tools such as Prometheus, Grafana, or similar.
• Familiarity with orchestration platforms including Dagster, Airflow, Prefect, or related technologies.

Desirable experience includes:

• Model serving infrastructure for real-time or batch inference workloads.
• Multi-tenant platform environments.
• Vector databases such as Qdrant.
• Development of high-performance APIs and data-intensive services.
• Broader Azure ecosystem expertise including Azure Container Registry, Azure Blob Storage, Azure Monitor, and Azure Key Vault.

What They Offer

• The opportunity to work on innovative AI and machine learning projects with genuine societal impact.