Production ML Engineer - Build Scalable Pipelines & MLOps

Production ML Engineer - Build Scalable Pipelines & MLOps

Full-Time 70000 - 90000 Β£ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Design and build robust data pipelines for scalable ML systems.
  • Company: Join Data Science Festival, a leader in AI and data innovation.
  • Benefits: Enjoy a competitive salary, generous holidays, and career progression.
  • Other info: Collaborative hybrid work environment with growth opportunities.
  • Why this job: Make a real impact on how millions engage with products through AI.
  • Qualifications: Experience in machine learning and data pipeline development.

The predicted salary is between 70000 - 90000 Β£ per year.

Data Science Festival is seeking a Machine Learning Engineer to design and build robust data pipelines while transforming ML prototypes into production-ready systems. This role directly influences how millions engage with products, ensuring data and AI strategies scale effectively.

You will work closely with analysts and data engineers in a hybrid setting, focusing on reliable, scalable models.

Benefits include competitive salary, generous holidays, and career progression opportunities.

Production ML Engineer - Build Scalable Pipelines & MLOps employer: Data Science Festival

At Data Idols, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our hybrid working model in London allows for flexibility while you contribute to a cutting-edge AI-powered platform, with ample opportunities for professional growth and influence over key architectural decisions. Join us to be part of a high-performing engineering team where your expertise will directly impact the future of technology in a supportive and forward-thinking environment.

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Contact Details:

Data Science Festival Recruitment Team

We think you need these skills to ace Production ML Engineer - Build Scalable Pipelines & MLOps

Machine Learning
Data Pipeline Design
MLOps
Model Deployment
Collaboration with Analysts
Collaboration with Data Engineers
Scalability