ML Ops Data Engineer

London - UK

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

ML Ops / Data Engineer

Role Overview

Were hiring an ML Ops Engineer / Data Engineer to own the reliability scalability and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data engineering and ML infrastructure: youll design and operate data pipelines that feed models and youll build the tooling that trains deploys monitors and retrains them.

Youll work closely with research engineers and product teams taking models from experimentation to production-grade systems with clear SLAs reproducibility guarantees and observable behaviour. This is not a research role; it is a hands-on engineering role focused on making ML systems work reliably at scale.

What Youll Work On

ML lifecycle infrastructure

Productionizing models: packaging deployment versioning and rollback
Designing CI/CD pipelines for ML (training validation deployment)
Implementing model monitoring (data drift prediction drift performance decay)
Managing experiment tracking and reproducibility

Data engineering foundations

Building and maintaining batch and nearreal-time data pipelines
Ensuring data quality schema evolution and lineage across systems
Designing datasets and feature pipelines that support both training and inference
Operating pipelines with clear reliability and latency expectations

Operational ownership

Defining and meeting availability latency and freshness targets for ML services
Debugging production issues across data infrastructure and model layers
Improving system robustness through automation and observability
Collaborating with platform and security teams on access secrets and compliance

Engineering rigor

Writing production-grade Python used in long-running services and pipelines
Establishing testing validation and release practices for ML systems
Making trade-offs explicit between research flexibility and production stability

Required Qualifications

37 years of professional experience in ML Ops Data Engineering or adjacent backend roles
Strong production Python skills (clean APIs testing performance awareness)
Experience deploying and operating ML models in production environments

Solid understanding of:

Model training vs. inference requirements
Batch vs. streaming data pipelines
Failure modes in data-driven systems
Hands-on experience with at least one modern orchestration or workflow system
Comfort working with cloud infrastructure and containerized workloads
Ability to reason about system design not just tool usage

Nice-to-Have

Experience operating systems at TB-scale data volumes or higher
Prior ownership of model monitoring drift detection or automated retraining
Familiarity with feature stores or online/offline feature consistency problems
Experience supporting multiple models or teams on a shared ML platform
Exposure to regulated or high-reliability production environments

Tech Stack (Current & Expected Evolution)

Languages: Python (core)

ML & Data: PyTorch / similar frameworks experiment tracking structured datasets

Pipelines & Orchestration: Workflow schedulers for batch and near-real-time processing

Deployment: Containers model serving frameworks infrastructure-as-code

Observability: Metrics logging and alerting across data and model layers

Cloud: Managed compute storage and networking (provider-agnostic mindset)

The stack will evolve. We value engineers who understand why systems are built a certain way and can adapt tools as requirements change.

Why This Role Matters

Our models only create value when they are correct observable and dependable in production. This role is responsible for that reality. Youll reduce the gap between promising experiments and systems that can be trusted by downstream products and customers.

If you care about data correctness operational clarity and building ML systems that dont silently fail this role gives you direct leverage over the success of our entire ML platform.

CMC Markets is an equal opportunities employer and positively encourages applications from suitably qualified and eligible candidates regardless of gender sexual orientation marital or civil partner status gender reassignment race colour nationality ethnic or national origin religion or belief disability or age.

Required Experience:

ML Ops / Data EngineerRole OverviewWere hiring an ML Ops Engineer / Data Engineer to own the reliability scalability and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data engineering and ML infrastructure: youll design and oper...