ML Ops Engineer

Overview

In this ML Ops Engineer role, you will architect and operate the platform that moves ML models from experimentation to reliable production services. You will own automation, deployment, observability and governance across the ML lifecycle, collaborating with research, software, platform and product teams. You’ll build repeatable workflows, ensure reproducibility, and manage model serving with robust monitoring and incident response. This is a hands-on role focused on scalable, secure ML systems that deliver value reliably.

Responsibilities

• Build repeatable workflows for model training, validation, promotion, deployment and retraining
• Productionise models via packaging, versioning, model registry integration, deployment automation and safe rollback
• Design CI/CD pipelines for ML systems with automated testing, validation and environment promotion
• Manage experiment tracking, model metadata and reproducibility across research and production
• Develop reusable tooling and platform capabilities supporting multiple models and teams
• Deploy and operate batch and online inference services in containerised cloud environments
• Define availability, latency, throughput and recovery objectives for ML services
• Monitor health, data quality signals, drift, and model performance decay with dashboards and alerts
• Support automated or controlled retraining, promotion, rollback and retirement of models
• Debug production issues across model, application, infra and data-dependency layers
• Improve system robustness, scalability and cost efficiency through automation and IaC
• Write production-grade Python for long-running services, tooling and ML workflows
• Establish testing, validation, release and incident-management practices for ML systems
• Collaborate with platform, security and data engineering on inputs, access controls, secrets and resilience

Key requirements

• 3–7 years’ experience in MLOps or related field
• Strong production Python skills with clean APIs and maintainable services
• Experience deploying and operating ML models in production
• Understanding of the ML lifecycle (training, validation, inference, retraining)
• Experience designing CI/CD workflows for ML or production software
• Hands-on with at least one workflow/orchestration system for ML
• Cloud, containers, IaC and service networking experience
• Strong observability, monitoring, alerting and incident response knowledge
• Ability to reason about system design, reliability and trade-offs
• Clear communication with cross-functional teams

• strong communication
• problem-solving mindset
• collaboration across disciplines
• Python
• PyTorch or similar ML frameworks
• model registries and experiment tracking tools

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