AI Platform & DevOps Engineer | AWS, Kubernetes in London

AI Platform & DevOps Engineer | AWS, Kubernetes in London

London Full-Time 80000 - 100000 £ / year (est.) No working from home possible
DGH Recruitment Ltd

At a Glance

  • Tasks: Build and scale infrastructure for advanced AI solutions with a focus on reliability.
  • Company: Join a leading organisation driving high-profile AI initiatives.
  • Benefits: Competitive salary of £80,000 - £100,000 and opportunities for professional growth.
  • Why this job: Be at the forefront of AI technology and make a significant impact.
  • Qualifications: Experience in MLOps, AWS, and Kubernetes is essential.

The predicted salary is between 80000 - 100000 £ per year.

We are partnering with a leading organisation on a high-profile AI initiative and are seeking an experienced MLOps / Platform Engineer to build and scale infrastructure supporting advanced AI solutions. This is a full-time onsite role where you will work closely with Data Science and Engineering teams to design and operate a secure, scalable platform for deploying AI workloads and agents.

Salary is £80,000 - £100,000 per year, with a strong emphasis on reliability and deployment pipelines.

AI Platform & DevOps Engineer | AWS, Kubernetes in London employer: DGH Recruitment Ltd

DGH Recruitment Ltd is an excellent employer, offering a dynamic work environment where innovation meets collaboration. Employees benefit from a culture that prioritises professional growth through continuous learning opportunities and access to cutting-edge technology, all while being part of a global transformation initiative. Located in a vibrant area, the company fosters a supportive atmosphere that encourages creativity and teamwork, making it an ideal place for those seeking meaningful and rewarding careers.

DGH Recruitment Ltd

Contact Details:

DGH Recruitment Ltd Recruitment Team

We think you need these skills to ace AI Platform & DevOps Engineer | AWS, Kubernetes in London

MLOps
Platform Engineering
AWS
Kubernetes
Infrastructure Design
AI Workloads Deployment
Scalability