MLOps and Cloud Ops Engineer
The Role
We are seeking a Senior MLOps & Cloud Operations Engineer to help design, build, and operate the cloud and machine‑learning infrastructure that supports production AI systems. This individual will own core platform components and ensure that machine learning workflows move seamlessly from development into stable, monitored, and compliant production environments.
This is a hands‑on, high‑ownership role with close collaboration across data science, engineering, and platform teams.
Key Responsibilities
- Design and maintain secure, highly available cloud environments that support AI and data workloads
- Operate and monitor core cloud and DevOps resources to ensure system reliability and performance
- Oversee enterprise‑scale data platforms and analytics environments, including storage strategy, lifecycle management, and job reliability
- Build and manage scalable infrastructure across AWS, Azure, and other cloud platforms as needed
- Develop and maintain CI/CD pipelines for machine learning workflows, covering training, testing, and deployment
- Implement standards for model versioning, artifact tracking, and experiment reproducibility
- Deploy and operate machine learning models using containerized and orchestrated solutions (e.g., Docker and Kubernetes)
- Monitor live ML systems for performance issues, anomalies, and model drift, and support iterative improvement
- Partner closely with data scientists, data engineers, and application teams to enable smooth ML operations
- Apply infrastructure‑as‑code and DevSecOps best practices, ensuring documentation and operational readiness
- Contribute to governance, security, compliance, and data‑privacy frameworks for AI systems
Required Experience & Skills
- 5+ years of experience in cloud infrastructure, DevOps, or MLOps roles
- Strong hands‑on experience with major cloud platforms such as AWS and/or Azure
- Experience supporting or operating Databricks environments
- Proficiency with infrastructure‑as‑code tools (e.g., Terraform, Pulumi, or similar)
- Solid experience with containerization and orchestration technologies (Docker, Kubernetes)
- Experience building CI/CD pipelines for machine learning or data systems
- Strong scripting and automation skills (Python, Bash, or equivalent)
- Familiarity with ML lifecycle tools and observability platforms for monitoring and alerting
Preferred Background
- Experience deploying and operating machine learning models in production environments
- Exposure to model monitoring, alerting, and retraining workflows
- Background working in regulated environments such as healthcare, finance, or life sciences
- Familiarity with security, compliance, and data‑privacy requirements (e.g., HIPAA, SOC 2)
FAQs
Congratulations, we understand that taking the time to apply is a big step. When you apply, your details go directly to the consultant who is sourcing talent. Due to demand, we may not get back to all applicants that have applied. However, we always keep your CV and details on file so when we see similar roles or see skillsets that drive growth in organisations, we will always reach out to discuss opportunities.
Yes. Even if this role isn’t a perfect match, applying allows us to understand your expertise and ambitions, ensuring you're on our radar for the right opportunity when it arises.
We also work in several ways, firstly we advertise our roles available on our site, however, often due to confidentiality we may not post all. We also work with clients who are more focused on skills and understanding what is required to future-proof their business.
That's why we recommend registering your CV so you can be considered for roles that have yet to be created.
Yes, we help with CV and interview preparation. From customised support on how to optimise your CV to interview preparation and compensation negotiations, we advocate for you throughout your next career move.