MLOps Engineer

About Akuna:

Akuna Capital is an innovative trading firm with a strong focus on collaboration cutting-edge technology data driven solutions and automation. We specialise in providing liquidity as an options market maker meaning we are committed to providing competitive quotes that we are willing to both buy and sell. To do this successfully we design and implement our own low latency technologies trading strategies and mathematical models. At Akuna we have a flat structure where the best idea wins.

Our Founding Partners including Andrew Killion first conceptualized Akuna in their hometown of Sydney. They opened the firms first office in 2011 in the heart of the derivatives industry and the options capital of the world Chicago. Today Akuna is proud to operate from additional offices in Sydney Shanghai Singapore and London.

What youll do as a MLOps Engineer at Akuna:

We are looking for an experienced MLOps Engineer to lead the direction of Akunas machine learning infrastructure and help enable the success of machine learning initiatives across the firm.
This role will work closely with teams across technology quantitative research and trading to support the full machine learning lifecycle: from research and experimentation through to deployment monitoring and ongoing improvement. You will help ensure current ML initiatives are successful while defining a clear roadmap toward scalable reliable and best-in-class ML infrastructure and practices.

In this role you will

• Design own and maintain the machine learning infrastructure partnering with cross functional teams across the lifecycle of key ML initiatives.
• Own and drive the roadmap for Akunas machine learning infrastructure tooling and processes.
• Take a hands-on role designing building and maintaining platforms that enable efficient model development training deployment monitoring and governance.
• Establish best practices for reproducible ML workflows model lifecycle management CI/CD observability and production reliability.
• Operate and continuously improve ML infrastructure to ensure it is scalable performant reliable and easy for researchers and engineers to use.
• Provide technical leadership and guidance to engineering and research teams adopting ML infrastructure capabilities.

Requirements:

• Bachelors degree or higher in Computer Science Engineering or a related technical field.
• Minimum 3 years full-time experience building deploying or operating machine learning infrastructure in production environments.
• Hands-on experience with ML infrastructure or data platforms such as Databricks or Spark.
• Strong programming skills in Python C or both in Linux-based environments.
• Effective communication skills with the ability to partner effectively across engineering research and business teams.
• Demonstrated ownership technical leadership and ability to define and execute a roadmap.

Please note:If you apply to multiple roles you may be asked to complete multiple coding challenges and interviews.