Akuna Capital

MLOpsEngineer

singapore Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“MLOps Engineer at Akuna Capital. Skills: Machine learning infrastructure, MLOps. Design, own and maintain the machine learning infrastructure. Partnering with cross functional teams across the lifecycle of key ML initiatives”

What They're Looking For.

Must Have

Bachelor's degree or higher in Computer Science, Engineering or a related technical field., 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., 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.

What You'll Do.

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 Akuna’s machine learning infrastructure

Take a hands-on role designing

and maintaining platforms that enable efficient model development

Establish best practices for reproducible ML workflows

model lifecycle management

and production reliability

Operate and continuously improve ML infrastructure to ensure it is scalable

and easy for researchers and engineers to use

Provide technical leadership and guidance to engineering and research teams adopting ML infrastructure capabilities

How You'll Work.

Team & Collaboration

partnering with cross functional teams across the lifecycle of key ML initiatives; partner effectively across engineering, research, and business teams; Provide technical leadership and guidance to engineering and research teams

Communication Scope

Effective communication skills; ability to partner effectively across engineering, research, and business teams

Process & Methodology

ability to define and execute a roadmap

Full Job Description

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 firm’s 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 you’ll do as a MLOps Engineer at Akuna: We are looking for an experienced MLOps Engineer to lead the direction of Akuna’s 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 Akuna’s 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,

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