

Senior Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Machine Learning Engineer in London (2x a week onsite) with a 6-month contract at £500 p/d (Outside IR35). Key skills include MLOps, Kubernetes, Python or Go, AWS, and CI/CD automation.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
496
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🗓️ - Date discovered
September 19, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
On-site
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📄 - Contract type
Outside IR35
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🔒 - Security clearance
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#ML Ops (Machine Learning Operations) #Grafana #Kubernetes #Docker #Monitoring #Programming #Deployment #Automation #Terraform #Prometheus #GitHub #AWS (Amazon Web Services) #Python #Observability #Cloud #ML (Machine Learning)
Role description
Senior Machine Learning Operations Engineer – London (2x a week onsite) - £500 p/d (Outside IR35) – 6-months Contract
We are seeking an experienced MLOps Engineer to join a globally known consumer tech company focused on building innovative, large-scale platforms. In this role, you will evolve and scale the machine learning platform, ensuring it supports high-throughput model inference and fast iteration cycles. This position is perfect for someone who thrives at the intersection of MLOps, Kubernetes, and cloud infrastructure, with a hands-on approach to solving complex challenges.
You will work closely with ML engineers and product teams to align infrastructure with evolving project needs, research and implement cutting-edge MLOps practices, and mentor colleagues by sharing expertise in cloud operations and ML engineering best practices.
The right candidate will also be responsible for managing GPU-powered Kubernetes clusters, improving automation pipelines, and ensuring system reliability. Candidates should have experience building and managing Kubernetes clusters from scratch, configuring them manually using tools like kubeadm, and deploying applications with Helm—demonstrating true infrastructure-level expertise rather than just deploying services on managed platforms.
Key Skills
• MLOps & Kubernetes: GPU-enabled cluster management, built from scratch using kubeadm and Helm.
• Programming: Python or Go for ML automation workflows.
• Containerization: Docker and containerized application deployment.
• Cloud: AWS experience supporting ML workloads.
• CI/CD & Automation: ArgoCD, GitHub Actions, Infrastructure-as-Code (Terraform).
• Monitoring & Observability: Prometheus, Grafana, cloud-native stacks.
• ML Lifecycle: Production experience with experimentation, training, deployment, versioning, and monitoring.
• Reliability & Support: On-call participation, incident response, and system optimization.
The ideal candidate will be a Senior MLOps Engineer with extensive, hands-on experience in Kubernetes, ready to make an impact at a globally recognised consumer tech company
Location: London (2x a week onsite)
Day rate: £500 p/d (Outside IR35)
Duration: 6-month
Senior Machine Learning Operations Engineer – London (2x a week onsite) - £500 p/d (Outside IR35) – 6-months Contract
We are seeking an experienced MLOps Engineer to join a globally known consumer tech company focused on building innovative, large-scale platforms. In this role, you will evolve and scale the machine learning platform, ensuring it supports high-throughput model inference and fast iteration cycles. This position is perfect for someone who thrives at the intersection of MLOps, Kubernetes, and cloud infrastructure, with a hands-on approach to solving complex challenges.
You will work closely with ML engineers and product teams to align infrastructure with evolving project needs, research and implement cutting-edge MLOps practices, and mentor colleagues by sharing expertise in cloud operations and ML engineering best practices.
The right candidate will also be responsible for managing GPU-powered Kubernetes clusters, improving automation pipelines, and ensuring system reliability. Candidates should have experience building and managing Kubernetes clusters from scratch, configuring them manually using tools like kubeadm, and deploying applications with Helm—demonstrating true infrastructure-level expertise rather than just deploying services on managed platforms.
Key Skills
• MLOps & Kubernetes: GPU-enabled cluster management, built from scratch using kubeadm and Helm.
• Programming: Python or Go for ML automation workflows.
• Containerization: Docker and containerized application deployment.
• Cloud: AWS experience supporting ML workloads.
• CI/CD & Automation: ArgoCD, GitHub Actions, Infrastructure-as-Code (Terraform).
• Monitoring & Observability: Prometheus, Grafana, cloud-native stacks.
• ML Lifecycle: Production experience with experimentation, training, deployment, versioning, and monitoring.
• Reliability & Support: On-call participation, incident response, and system optimization.
The ideal candidate will be a Senior MLOps Engineer with extensive, hands-on experience in Kubernetes, ready to make an impact at a globally recognised consumer tech company
Location: London (2x a week onsite)
Day rate: £500 p/d (Outside IR35)
Duration: 6-month