Senior ML Ops Engineer
Hybrid London Full-time Senior ML Platform & Ops
At a glance
Senior ML Platform & Ops role at Xantura. London · full-time · no salary stated · we estimate £96,000–£116,772 across the United Kingdom.
Estimated pay
Xantura has not published a salary for this role. The figure below is our estimate, not theirs.
£96,000 – £116,772
What the middle half of senior ML platform & ops roles in the United Kingdom paid, among postings that did state a salary.
Worked out from 5 postings that stated pay, median £116,772. Last updated Oct 13, 2026.
RoleML Platform & Ops
SenioritySenior
LocationLondon
WorkplaceHybrid
EmploymentFull-time
PostedJun 12, 2026 · 4mo ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Keep the infrastructure behind AI services evolving so it runs reliably as more local authority clients come on board
- Host and look after ML models through Azure ML and AKS, tracking their performance and condition over time
- Build Dagster workflows for training, predictions and retraining, checking that incoming council data is timely and usable
- Make ML systems traceable and explainable, following Responsible AI principles and UK GDPR
- Share technical guidance with delivery and consulting teams, including feasibility advice and input on governance
What you bring
- At least four years in MLOps, platform or infrastructure engineering for ML or data-heavy systems
- Production-level Python skills and hands-on experience with Azure’s MLOps services
- Practical production experience with Kubernetes, CI/CD, infrastructure-as-code, monitoring and pipeline orchestration
- A computer science, software engineering or related technical degree, or equivalent practical experience
Who this fits
This senior role suits an engineer who can run production ML systems and work across platform infrastructure, model deployment and governance. You’ll work from the London Borough office at least one or two days each week, with some travel for client visits as needed.
From the employer
In this role you will work in the Platform team – a function for the deployment and evolution of the backend platform that underpins the core of the Xantura business.
As an MLOps Engineer, you'll own the infrastructure layer that all of Xantura's AI services depend on to operate and scale. You'll be responsible for how ML models and NLP systems are deployed, monitored, and maintained across a growing base of local authority clients, ensuring that what works for one client works reliably for a hundred.
You'll work across every pillar of the AI function - predictive modelling, text analytics, knowledge representation, and agentic AI - building the deployment pipelines, orchestration, and observability that enable the rest of the engineering team to ship with confidence.
Key responsibilities
We’d love to hear from you if you have:
Bonus points if you have:
Location – This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office at least 1-2 days per week. Some travel is also required for on-site client engagements as needed.
As an MLOps Engineer, you'll own the infrastructure layer that all of Xantura's AI services depend on to operate and scale. You'll be responsible for how ML models and NLP systems are deployed, monitored, and maintained across a growing base of local authority clients, ensuring that what works for one client works reliably for a hundred.
You'll work across every pillar of the AI function - predictive modelling, text analytics, knowledge representation, and agentic AI - building the deployment pipelines, orchestration, and observability that enable the rest of the engineering team to ship with confidence.
Key responsibilities
- Continuously evolve the platform infrastructure powering all AI services (predictive modelling, NLP, knowledge representation, and agentic AI), ensuring reliable, scalable operation across a growing base of local authority clients.
- Deploy and manage ML models via Azure ML endpoints, batch endpoints , and AKS, enabling resilient, secure model hosting that accelerates client onboarding and ensures models remain performant and monitorable throughout their lifecycle.
- Ensure all ML systems are transparent, explainable, and auditable, aligned with Responsible AI principles and UK GDPR; essential where AI outputs inform decisions about vulnerable people in health and social care.
- Design, build, and maintain production-grade orchestration pipelines (Dagster) supporting model training, inference, and retraining, ensuring data from local authority systems is timely, accurate, and fit for purpose before it reaches ML services.
- Contribute to organisation-wide AI capability building, sharing best practice with delivery and consulting teams, advising on technical feasibility, and shaping governance standards as the AI function scales.
We’d love to hear from you if you have:
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
- 4+ years of professional experience in an MLOps, Platform Engineering, or Infrastructure Engineering role supporting ML or data-intensive systems.
- Strong programming skills and production experience in Python.
- Expertise in Azure-native MLOps, including model endpoints, pipelines, registries, environments, and compute management.
- Deploying, scaling, and troubleshooting containerised workload on Kubernetes in production
- Building and maintaining CI/CD pipelines (Azure DevOps or equivalent) for automated testing, building, and deployment of ML services
- Implementing infrastructure-as-code (Terraform, Bicep or Pulumi)
- Implementing monitoring and observability for production systems, including metrics, altering, logging, and dashboarding (e.g. Prometheus, Grafana)
- Pipeline orchestration using Dagster, Airflow, Prefect, or similar
Bonus points if you have:
- Practical experience with model serving infrastructure – batch and/or real-time inference at scale.
- Experience operating multi-tenant systems, particularly scaling infrastructure across multiple clients or business units.
- Practical experience building and serving production-ready, asynchronous APIs for embedding and/or other compute-intensive services.
- Experience setting up, and optimising vector databases, e.g. Qdrant, and integrating with other services
- Proficiency in Python for building high-performance data and model pipelines, with strong software engineering discipline (testing, versioning, CI/CD).
- Deep familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Container Registry, Azure DevOps, Azure Blob Storage, Azure Monitor, Azure Key Vault).
Location – This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office at least 1-2 days per week. Some travel is also required for on-site client engagements as needed.