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).