AI Connect | Data & AI Delivery Partner
AI Platform Engineering Lead
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AI Platform Engineering Lead – Fully Remote
Competitive Six Figure Salary & Package
The Opportunity
Our client is building a next-generation AI platform capability to support advanced Machine Learning and Generative AI research. As part of a newly established platform team, you'll help design, build, and operate the infrastructure that enables AI Researchers and ML Engineers to develop, train, experiment with, and deploy cutting-edge AI systems at scale.
This is not a traditional DevOps or MLOps role. The focus is on creating a robust, self-service engineering platform that acts as an internal product, supporting everything from model experimentation and distributed training through to production deployment and inference. You'll work at the intersection of Platform Engineering, Cloud Infrastructure, Kubernetes, and AI, helping shape how AI research is enabled across the organisation.
What You'll Do
- Design, build, and operate a scalable ML platform that supports the full machine learning lifecycle.
- Develop and maintain Kubernetes-based infrastructure supporting AI, ML, and Generative AI workloads.
- Build self-service capabilities that enable researchers and engineers to train, deploy, and manage models independently.
- Support GPU-accelerated environments used for model training, experimentation, and inference workloads.
- Design and implement infrastructure using Infrastructure as Code and modern cloud engineering practices.
- Develop platform observability, monitoring, alerting, and operational tooling.
- Work closely with AI Researchers, ML Engineers, and Software Engineers to understand and support evolving platform requirements.
- Contribute to platform architecture decisions, engineering standards, and long-term technical strategy.
- Support hybrid cloud environments spanning AWS and on-premise infrastructure.
- Help evaluate and adopt new technologies that improve platform scalability, reliability, and developer experience.
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Key Skills & Experience
- Strong experience designing and operating Kubernetes platforms in production environments.
- Hands-on experience administering and scaling Kubernetes clusters, ideally within AWS (EKS).
- Strong cloud engineering experience within AWS environments.
- Experience building platform infrastructure from the ground up rather than solely supporting existing environments.
- Strong understanding of Infrastructure as Code, automation, CI/CD, monitoring, and observability practices.
- Experience working with distributed systems and high-performance compute environments.
- Strong software engineering or scripting skills, ideally using Python.
- Experience supporting GPU-based workloads is highly desirable.
- Exposure to ML infrastructure, MLOps tooling, training pipelines, model deployment, or inference platforms is advantageous.
- Strong troubleshooting and problem-solving skills across infrastructure and platform environments.
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