AI Summary
Design, build, and maintain machine learning and AI infrastructure platform. Create robust, scalable platforms for ML model deployment and inference. Collaborate with data science teams to optimize deployment workflows.
Key Highlights
Design and implement scalable ML/AI platforms
Manage and optimize GPU cluster resources
Implement monitoring, logging, and alerting systems
Collaborate with data science teams to optimize deployment workflows
Technical Skills Required
Benefits & Perks
Right to Work in the country of application
Diversity & Inclusion
Respect, openness, and trusted collaboration
Job Description
Company
team.blue is an ecosystem of 60+ successful brands working together across 22 European countries to provide its 3.5 million SMB customers with everything they need to succeed online by offering best-in-class expertise and services.
team.blue's brands are a mix of traditional hosting businesses that offer services from domain names, email, shared hosting, e-commerce, and server hosting solutions and, as specialist SaaS providers, adjacent products such as compliance, marketing tools, and team collaboration products. This broad product offering makes it a one-stop partner for online businesses and entrepreneurs across Europe.
Position
We are looking for an experienced Senior AI/ML Platform Engineer to design, build, and maintain our machine learning and AI infrastructure platform. This role is critical to enabling our data science and AI teams to deploy, scale, and manage ML models efficiently across multi-GPU environments. You'll be responsible for creating robust, scalable platforms that support the full ML lifecycle from model training to inference, with a particular focus on LLM deployment and management.
Key Responsibilities
Platform Development & Management
- Design and implement scalable ML/AI platforms supporting model deployment across multi-GPU nodes
- Build and maintain infrastructure for LLM inference serving, including optimization for latency and throughput
- Develop automated deployment pipelines for machine learning models using containerization and orchestration technologies
- Create self-service tools and APIs that enable data scientists to deploy models independently
- Manage and optimize GPU cluster resources, ensuring efficient utilization and cost management
- Implement monitoring, logging, and alerting systems for ML workloads and model performance
- Design disaster recovery and backup strategies for critical ML infrastructure
- Maintain high availability and reliability standards for production ML services
- Build CI/CD pipelines specifically tailored for ML model deployment and updates
- Automate infrastructure provisioning using Infrastructure as Code (IaC) principles
- Implement model versioning, rollback capabilities, and A/B testing frameworks
- Develop automated scaling solutions for varying inference workloads
- Work closely with data science teams to understand requirements and optimize deployment workflows
- Provide technical guidance on best practices for model deployment and infrastructure usage
- Collaborate with security teams to implement secure ML model serving practices
- Document platform capabilities, procedures, and troubleshooting guides
Professional Experience
- 4+ years of experience in Platform engineering, DevOps, or infrastructure roles
- 2+ years of experience specifically with ML/AI infrastructure or platforms
- Cloud Platforms: 4+ years experience with AWS, Azure, or GCP, particularly GPU-enabled services
- Containerization: Proficiency with Docker and Kubernetes, including GPU scheduling and resource management
- Infrastructure as Code: Experience with Terraform, CloudFormation, or similar tools
- Programming: Strong skills in Python and at least one additional language (Go, Java, or Rust)
- ML Frameworks: Familiarity with PyTorch, TensorFlow, and model serving frameworks (TorchServe, TensorFlow Serving, etc.)
- Experience building and maintaining production ML platforms or similar infrastructure (KubeFlow, MLFlow, SageMaker, etc)
- Knowledge of GPU computing, CUDA, and multi-GPU distributed computing
- Understanding of ML model lifecycle management and MLOps practices
- Experience with monitoring tools (Prometheus, Grafana, ELK stack)
- Experience with streaming data processing (Kafka, Kinesis, Pulsar)
- Familiarity with service mesh technologies and API gateways
- Understanding of large language models (LLMs) and inference optimization techniques
- Knowledge of model quantization, pruning, and other optimization methods
- Experience with distributed training and inference across multiple GPUs/nodes
- Familiarity with vector databases and embedding storage solutions
At any stage, please be prepared to provide proof of eligibility to work in the country you’re applying for. Unfortunately, we are unable to support relocation packages or sponsorship visas.
ESG
“At team.blue, our commitment to caring for the environment and each other is at the heart of everything we do. Our latest impact report showcases our ongoing ESG efforts and ambitious sustainability goals. Interested in learning more about our dedication to making a positive impact? Check it out here.”
"Come as you are"
Everyone is welcome here. Diversity & Inclusion are at our core. Far above any technical competence, we value respect, openness, and trusted collaboration. We do not tolerate intolerance.
The most trusted digital enabler
team.blue is a leading digital enabler for companies and entrepreneurs. It serves over 3.3 million customers in Europe and has more than 3,000 experts to support them. Its goal is to shape technology and to empower businesses with innovative digital services.
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