ML Ops Engineer

ML Ops EngineerRole OverviewWe’re hiring an ML Ops 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: you’ll design and operate data pipelines that feed models, and you’ll build the tooling that trains, deploys, monitors, and retrains them.You’ll 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 You’ll 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 near–real-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 long-running services and pipelinesEstablishing testing, validation, and release practices for ML systemsMaking trade-offs explicit between research flexibility and production stabilityRequired Qualifications3–7 years of professional experience in ML Ops, Data Engineering, or adjacent backend rolesStrong production Python 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. You’ll 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 don’t 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.SummaryLocation: London; WarsawType: Full time