ML Engineer - Python, AWS & MLOps (Hybrid) in London

ML Engineer - Python, AWS & MLOps (Hybrid) in London

London Full-Time 60000 - 80000 £ / year (est.) No working from home possible
Client Server

At a Glance

  • Tasks: Build and maintain ML infrastructure, deploy models, and develop CI/CD pipelines.
  • Company: Join a dynamic team in London focused on innovative ML solutions.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Exciting environment with a focus on innovation and career advancement.
  • Why this job: Work on cutting-edge fraud detection projects and collaborate with diverse teams.
  • Qualifications: Strong Python skills, AWS experience, and knowledge of scalable data pipelines.

The predicted salary is between 60000 - 80000 £ per year.

Client Server is seeking a skilled ML/MLOps engineer to join our London team.

You will build and maintain ML infrastructure, deploy models, and develop CI/CD pipelines in a hybrid work setting.

The role requires strong Python skills, AWS experience, and a solid background in scalable data pipelines and ML platforms.

You will work on cutting‑edge fraud detection and data engineering tasks while collaborating with cross‑functional teams.

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ML Engineer - Python, AWS & MLOps (Hybrid) in London employer: Client Server

Client Server is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With competitive salaries, comprehensive benefits including life assurance and a wellness package, and the flexibility to work from home, employees are empowered to thrive both personally and professionally. Join a highly profitable Hedge Fund where your skills will be valued, and you will have ample opportunities for career progression in a senior role.

Client Server

Contact Details:

Client Server Recruitment Team

We think you need these skills to ace ML Engineer - Python, AWS & MLOps (Hybrid) in London

Python
AWS
MLOps
ML Infrastructure
CI/CD Pipelines
Data Engineering
Scalable Data Pipelines