Machine Learning Infrastructure Engineer

3 weeks ago

London, Greater London, United Kingdom Hunter Bond Full time £160,000
Machine Learning Engineer for Trading Firm

Are you a skilled engineer with a passion for cutting-edge technology? We are partnering with a leading trading firm that is seeking a talented Machine Learning Engineer to join their expanding team.

This is a fantastic opportunity to shape the future of ML infrastructure in a high-impact environment, supporting the deployment of advanced ML trading models.

Key Responsibilities:
  • Design, build, and optimize scalable ML pipelines for deploying machine learning models used in trading strategies.
  • Collaborate closely with data scientists, quants, and software engineers to enhance and automate ML workflows.
  • Ensure reliability, scalability, and security of ML systems across development, testing, and production environments.
  • Lead initiatives in CI/CD, monitoring, and orchestration, ensuring seamless integration of new ML models.
  • Stay ahead of industry trends and implement state-of-the-art ML Ops tools and frameworks to improve model delivery and deployment processes.
What You'll Get:
  • Unmatched career growth: Develop your expertise in a forward-thinking, technology-driven environment with rapid career progression.
  • Gain hands-on experience with some of the most cutting-edge tools and frameworks in the ML and FinTech space.
  • Enjoy an exceptional benefits package including industry-leading bonuses, healthcare, and reimbursement programs.
  • Flexible working environment: Balance work and life with hybrid working options, prioritizing your well-being and professional satisfaction.
  • Be part of a team that pushes the boundaries of ML technology in one of the most competitive sectors globally.
Requirements:
  • Degree in Computer Science, Engineering, Mathematics, or other STEM fields.
  • 5+ years of experience in ML Ops, DevOps, or related roles, with strong proficiency in Python and modern software development practices.
  • Proven experience with version control systems (Git), CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, or Azure).
  • Demonstrated enthusiasm for staying updated with the latest developments in ML Ops and adopting new technologies quickly.

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