machine learning engineer

CMC Markets provides financial services and investment banking solutions. The company focuses on online trading and financial technology, offering platforms for retail and institutional clients to trade various financial instruments.

• Design and operate data pipelines for model training and inference;
• Build tooling for training, deploying, monitoring, and retraining machine learning models;
• Productionize models including packaging, deployment, versioning, and rollback;
• Design ML CI/CD pipelines covering training, validation, and deployment;
• Implement model monitoring for data drift, prediction drift, and performance decay;
• Manage experiment tracking and reproducibility;
• Build and maintain batch and near-real-time data pipelines;
• Ensure data quality, schema evolution, and lineage;
• Define and meet availability, latency, and freshness targets for ML services;
• Debug production issues across data, infrastructure, and models;
• Improve system robustness through automation and observability;
• Collaborate with platform and security teams on access, secrets, and compliance;
• Establish testing, validation, and release practices for ML systems.

• 3–7 Years of professional experience in ML Ops, Data Engineering, or backend roles;
• Strong production Python skills including clean APIs, testing, and performance awareness;
• Experience deploying and operating ML models in production environments;
• Solid understanding of model training vs. inference requirements, batch vs. streaming data pipelines, and 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;
• Nice to have: Experience operating systems at TB-scale data volumes, prior ownership of model monitoring or automated retraining, familiarity with feature stores, experience supporting multiple models or teams on a shared ML platform, exposure to regulated or high-reliability production environments.

• No conditions specified