Machine Learning Infrastructure Engineer

3 days ago


London Area, United Kingdom Hunter Bond Full time
Job Title: Machine Learning Infrastructure Engineer

Company: Hunter Bond

Salary: Up to £160,000 + Performance-Based Bonuses

Location: London / Hybrid (Flexible working model)

We are seeking a highly skilled Machine Learning Infrastructure Engineer to join our expanding team. As a key member of our Machine Learning Infrastructure Core team, you will play a crucial role in shaping the future of ML infrastructure in a high-impact environment.

Roles & 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's in it for you?
  • Unmatched career growth: Develop your expertise in a forward-thinking, technology-driven environment with rapid career progression.
  • Exposure to advanced technology: Gain hands-on experience with some of the most cutting-edge tools and frameworks in the ML and FinTech space.
  • Comprehensive benefits: 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.
  • Innovation at scale: Be part of a team that pushes the boundaries of ML technology in one of the most competitive sectors globally.
Key Requirements:
  • Educational background: Degree in Computer Science, Engineering, Mathematics, or other STEM fields.
  • Technical expertise: 5+ years of experience in ML Ops, DevOps, or related roles, with strong proficiency in Python and modern software development practices.
  • Hands-on skills: Proven experience with version control systems (Git), CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, or Azure).
  • Strong communicator: Excellent written and verbal communication skills, with the ability to collaborate across teams and explain complex concepts clearly.
  • Problem solver: Ability to work in fast-paced, high-pressure environments, with a proactive approach to tackling new challenges.
  • Passion for learning: Demonstrated enthusiasm for staying updated with the latest developments in ML Ops and adopting new technologies quickly.


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