Machine Learning Operations Engineer
Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on Sep 12, 2026. CMC Markets scores B on the Alion truth index.
Join CMC Markets as an ML Ops Engineer, where you'll be responsible for building and operating the platform capabilities that take machine-learning models from experimentation to reliable production services. This hands-on engineering role focuses on making ML systems reproducible, scalable, secure, and dependable. You'll work closely with research engineers, software engineers, platform teams, and product teams.
Missions
- Automatiser, déployer, observer et contrôler les opérations autour du cycle de vie de l'apprentissage automatique.
- Construire des flux de travail répétables pour l'entraînement, la validation, la promotion, le déploiement et le réentraînement des modèles.
- Déployer et exploiter des services d'inférence batch et en ligne dans des environnements cloud conteneurisés.
Profil recherché
- Comfort working with cloud infrastructure, containers, infrastructure as code and service networking- Clear communication skills and the ability to work effectively with research, engineering, platform, security and product teams
- Ability to reason about system design, reliability and operational trade-offs-not just individual tools
- Hands-on experience with at least one workflow or orchestration system used for ML training, validation or deployment
- Experience designing CI/CD workflows and release processes for ML or other production software systems
- Experience deploying, serving and operating machine-learning models in production environments
- 3-7 years’ professional experience in MLOps, ML platform engineering, ML infrastructure, backend engineering, DevOps or SRE
- Strong understanding of observability, monitoring, alerting, incident response and common failure modes in ML systems
- Strong production Python skills, including clean APIs, testing, performance awareness and maintainable services
- Practical understanding of the ML lifecycle, including training, validation, inference, model release, monitoring and retraining
- Experience with PyTorch or similar ML frameworks and model-serving technologies
- Exposure to regulated or high-reliability production environments
- Experience supporting multiple models, services or teams on a shared ML platform
- Familiarity with model registries, feature stores and offline/online feature-consistency challenges
- Prior ownership of model monitoring, drift detection or automated retraining