MLOps Engineer β€” Generative AI & Scalable Production

MLOps Engineer β€” Generative AI & Scalable Production

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
KDR Recruitment USA

At a Glance

  • Tasks: Build scalable ML pipelines and deploy models in a cutting-edge AI environment.
  • Company: Join a tech-driven insights organisation focused on generative AI.
  • Benefits: Enjoy remote work, competitive salary, and opportunities for professional growth.
  • Other info: Collaborate with top researchers and engineers in a dynamic team.
  • Why this job: Make an impact by bridging research and production in the exciting field of AI.
  • Qualifications: Experience in MLOps and cloud-native ML systems is essential.

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

KDR Recruitment USA is partnering with a technology-driven insights organisation to hire an AI & MLOps Engineer for UK remote work, focused on generative AI and cloud-native ML systems.

You will join a synthetic data team, bridging research and production, building scalable ML pipelines, deploying models, maintaining CI/CD, and optimizing cloud infrastructure across GPU/CPU.

Collaboration with researchers and engineers is essential.

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MLOps Engineer β€” Generative AI & Scalable Production employer: KDR Recruitment USA

Join a forward-thinking global insights and research technology company that champions innovation and collaboration in the heart of the UK. With a strong emphasis on employee growth, we offer a dynamic work culture that encourages creativity and agility, alongside competitive benefits and opportunities for professional development in cutting-edge AI and ML projects. Experience the unique advantage of working in a hybrid/remote environment that promotes work-life balance while being part of a passionate team dedicated to delivering impactful solutions.

KDR Recruitment USA

Contact Details:

KDR Recruitment USA Recruitment Team

We think you need these skills to ace MLOps Engineer β€” Generative AI & Scalable Production

MLOps
Generative AI
Cloud-Native ML Systems
Scalable ML Pipelines
Model Deployment
CI/CD Maintenance
Cloud Infrastructure Optimization