Data Engineer
Role details
Job location
Tech stack
Job description
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 OnML lifecycle infrastructureProductionizing models: packaging, deployment, versioning, and rollbackDesigning CI/CD pipelines for ML (training --> validation --> deployment)Implementing model monitoring (data drift, prediction drift, performance decay)Managing experiment tracking and reproducibilityData engineering foundationsBuilding and maintaining batch and nearreal-time data pipelinesEnsuring data quality, schema evolution, and lineage across systemsDesigning datasets and feature pipelines that support both training and inferenceOperating pipelines with clear reliability and latency expectationsOperational ownershipDefining and meeting availability, latency, and freshness targets for ML servicesDebugging production issues across data, infrastructure, and model layersImproving system robustness through automation and observabilityCollaborating with platform and security teams on access, secrets, and complianceEngineering rigorWriting production-grade Python used in, skills (clean APIs, testing, performance awareness)Experience deploying and operating ML models in production environmentsSolid understanding of:Model training vs. inference requirementsBatch vs. streaming data pipelinesFailure modes in data-driven systemsHands-on experience with at least one modern orchestration or workflow systemComfort working with cloud infrastructure and containerized workloadsAbility to reason about system design, not just tool usageNice-to-HaveExperience operating systems at TB-scale data volumes or higherPrior ownership of model monitoring, drift detection, or automated retrainingFamiliarity with feature stores or online/offline feature consistency problemsExperience supporting multiple models or teams on a shared ML platformExposure to regulated or high-reliability production environmentsTech Stack (Current & Expected Evolution)Languages:
Python (core)ML & Data:
PyTorch / similar frameworks, experiment tracking, structured datasetsPipelines & Orchestration:
Workflow schedulers for batch and near-real-time processingDeployment:
Containers, model serving frameworks,
infrastructure-as-codeObservability:
Metrics, logging, and alerting across data and model layersCloud:
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 MattersOur 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., Looking for a senior Fin Ops Engineering. In this pivotal role, you'll enable teams to build cost-effective solutions on Microsoft Azure while maintaining agility and fostering innovation. This position is perfect for engineers who are passionate about optimising cloud..., Data Engineer (II) | Snowflake & Microsoft Fabric Location: London Work Mode: Hybrid(1-2 days/week in Office) Duration: 6-Month Initial Contract Start Date: ASAP We are seeking a proactive Data Engineer (II) to build and scale high-performance data pipelines across..., Senior Data Engineer - Storage Optimisation Engagement London (Hybrid) Starting ASAP for an initial 8 week engagement Outside IR35 - rate depending on experienceWe're supporting a client looking for a Senior Data Engineer to lead a short-term, high-impact storage..., Looking for a senior Fin Ops Engineering. In this pivotal role, you'll enable teams to build cost-effective solutions on Microsoft Azure while maintaining agility and fostering innovation. This position is perfect for engineers who are passionate about optimising cloud... © 2026, Jobsora.com
Requirements
long-running services and pipelinesEstablishing testing, validation, and release practices for ML systemsMaking trade-offs explicit between research flexibility and production stability
Required Qualifications37 years of professional experience in
ML Ops, Data Engineering, or adjacent backend rolesStrong
production Python, Role Title: Databricks Data EngineerBU/Segment: Insurance DomainLocation: London, United Kingdom (Flexible Hybrid)Employment Type: PermanentNO SponsorshipQualifications and experience we consider to be essential for the role:7-12 years of experience in Data Engineering,...
