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Data Platform Engineer/ML Ops

2 months ago

United Kingdom Deep Medical Full time

Job title: Data Platform Engineer, MLOps

About Deep Medical:

Deep Medical is a pioneering medical technology company dedicated to leveraging artificial intelligence and machine learning to enhance healthcare delivery. Our mission is to develop innovative solutions that improve patient outcomes, streamline clinical workflows, and optimise resource utilisation within the healthcare system. Our technologies are extremely high impact and have the potential to save hundreds of thousands of lives by ensuring patients access the life-saving care they need and reducing health inequalities.

Position Overview:

As a data platform engineer, MLOps, you will be critical in supporting and enhancing our technology infrastructure, specifically focused on deploying machine learning (ML) services, managing large-scale PostgreSQL databases, and leveraging the extensive capabilities of AWS. You will work across the ML and Platform teams to ensure seamless integration of ML models into production environments, optimising performance, scalability, and reliability.

Responsibilities:

  • Design, deploy, and manage scalable, high-availability systems on AWS, including data warehousing, data lakes, and large PostgreSQL databases for feature storage and retrieval.
  • Use Pandas or Spark to manipulate and process large datasets
  • Implement and maintain FastAPI endpoints to serve as tools and interfaces for internal services.
  • Identity opportunities to improve the ML model lifecycles by using tools that improve model experimentation.
  • Implement strong testing and CI/CD practices to ensure reliable and confident ML service development and deployment.
  • Collaborate with platform engineers, ML engineers, and interdisciplinary teams to translate research into production-ready solutions.
  • Ensure compliance with data privacy regulations, cloud security best practices, and provide technical guidance and support to the team.

Must haves:

  • 3+ years of hands-on experience with AWS services (e.g., EC2, RDS, S3, SageMaker, Lambda) and AWS data warehousing and data lake solutions.
  • 3+ years of hands-on experience with large relational databases
  • 5+ Experience with Python API frameworks (e.g., FastAPI, Django, Flask) for developing RESTful services and Python modules such as Pandas or Spark.
  • AWS certifications such as AWS Solutions Architect or AWS DevOps Engineer.

Requirements:

  • Master’s or Ph.D. in Computer Science, Electrical Engineering, Statistics, or a related field.
  • Experience with container orchestration technologies such as Docker.
  • Familiarity with CI/CD pipelines and automation tools like GitLab.
  • Proven expertise in deploying and managing machine learning models in production environments, using MLOps tools like MLFlow, TensorBoard, or Weights & Biases.
  • Solid understanding of cloud security practices, data protection strategies, and cloud computing platforms, particularly AWS.
  • Proficiency in Python and experience with libraries such as PyTorch and scikit-learn; experience with TensorFlow is a plus.
  • Demonstrated ability to lead projects from conception to delivery, with a focus on innovation, impact, and collaboration within multidisciplinary teams.