ML Ops Engineer

We’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 On ML lifecycle infrastructure - Productionizing models: packaging, deployment, versioning, and rollback - Designing ML CI/CD pipelines (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 near-real-time data pipelines - Ensuring data quality, schema evolution, and lineage - Designing datasets and feature pipelines for training and inference - Operating pipelines against reliability and latency targets Operational ownership - Defining and meeting availability, latency, and freshness targets for ML services - Debugging production issues across data, infrastructure, and models - Improving robustness through automation and observability - Collaborating with platform and security teams on access, secrets, and compliance Engineering rigor - Writing production-grade Python for services and pipelines - Establishing testing, validation, and release practices for ML systems - Balancing research flexibility with production stability through explicit trade-offs Required Qualifications - 3 - 7 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 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. 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.