ML Ops Engineer
CMC Markets1 day ago
London, United KingdomMid Level / Senior
Responsibilities
- Build repeatable workflows for ML training, validation, model promotion, deployment, retraining, rollback, and retirement.
- Productionise models through packaging, versioning, model registry integration, deployment automation, and safe release controls.
- Design CI/CD pipelines and reusable platform tooling for ML systems and engineering teams.
- Deploy and operate batch and online inference services in containerised cloud environments.
- Define and monitor availability, latency, throughput, recovery, service health, data quality, data drift, prediction drift, and model performance objectives.
- Build dashboards, alerting, operational runbooks, and incident-management practices for ML systems.
- Write production-grade Python for long-running services, deployment tooling, and ML workflows.
- Improve robustness, scalability, security, resilience, and cost efficiency through automation and infrastructure as code.
- Collaborate with platform, security, data engineering, research, software engineering, and product teams on reliable inputs, access controls, secrets, compliance, and operational trade-offs.
Requirements
- 3–7 years of professional experience in MLOps, ML platform engineering, ML infrastructure, backend engineering, DevOps, or SRE.
- Strong production Python skills, including clean APIs, testing, performance awareness, and maintainable services.
- Experience deploying, serving, and operating machine-learning models in production environments.
- Practical understanding of the ML lifecycle, including training, validation, inference, model release, monitoring, and retraining.
- Experience designing CI/CD workflows and release processes for ML or other production software systems.
- Hands-on experience with at least one workflow or orchestration system used for ML training, validation, or deployment.
- Experience with cloud infrastructure, containers, infrastructure as code, and service networking.
- Strong understanding of observability, monitoring, alerting, incident response, and common ML-system failure modes.
- Ability to reason about system design, reliability, and operational trade-offs beyond individual tools.
- Clear communication and effective collaboration with research, engineering, platform, security, and product teams.
- Prior ownership of model monitoring, drift detection, or automated retraining is desirable.
- Familiarity with model registries, feature stores, and offline/online feature-consistency challenges is desirable.
- Experience supporting multiple models, services, or teams on a shared ML platform is desirable.
- Exposure to regulated or high-reliability production environments is desirable.
- Experience with PyTorch or similar ML frameworks and model-serving technologies is desirable.
Categories
About CMC Markets
CMC Markets builds online trading and investing platforms for retail and institutional clients, covering CFDs, forex, indices, commodities and share trading, plus Australian stockbroking. The London‑headquartered company, founded in 1989, is publicly listed on the London Stock Exchange and included in the FTSE 250. Revenue comes from spreads, commissions and financing, and offerings span retail platforms (CMC Markets/Invest) and institutional liquidity and APIs (CMC Connect).