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Machine Learning Engineer

Opus Recruitment Solutions
Nottingham
5 months ago
Applications closed

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Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Position Overview:

I am working with a client who is seeking a talented Senior Machine Learning Engineer with expertise in Generative AI to join their dynamic team. This role involves designing, developing, and implementing advanced generative models specifically for healthcare applications. As a senior engineer, you will lead the creation of innovative AI solutions, optimise model performance, and collaborate closely with various teams.


Key Responsibilities:

  • Lead experimentation and benchmarking efforts to assess model performance and robustness.
  • Utilize Bayesian Optimization, Reinforcement Learning, or Meta-Learning techniques to improve generative models.
  • Enhance deep learning architectures to boost efficiency, scalability, and readiness for deployment.
  • Innovate and implement cutting-edge generative models such as Diffusion Models, GANs, VAEs, and Transformers.
  • Collaborate with software engineers, researchers, and product managers to integrate models into production environments.
  • Partner with the CTO to shape the AI team's research strategy.
  • Uphold best practices in MLOps, including version control, CI/CD pipelines, and model monitoring.


Qualifications:

  • Proficiency in deep learning frameworks such as PyTorch, TensorFlow, and JAX.
  • Over 2 years of experience in machine learning, with a minimum of 2 years dedicated to generative AI.
  • Understanding of tokenization, embedding techniques, and multi-modal generative models.
  • Strong grasp of probabilistic models, variational inference, and generative modelling techniques.
  • Experience with data pipelines, feature engineering, and managing large-scale datasets.
  • Knowledge of LLMs (with less emphasis), Stable Diffusion, GANs, VAEs, and self-supervised learning paradigms.
  • Excellent problem-solving abilities, with a history of publishing research or contributing to open-source projects.
  • Experience in deploying models on cloud platforms (AWS, GCP, Azure) and using Kubernetes, Docker, and distributed computing frameworks (Ray, Dask, Spark).
National AI Awards 2025

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