Senior Machine Learning Engineer

ROLE OVERVIEW

You’ll work at the intersection of data science and engineering to build, deploy, and scale machine learning systems. This includes improving ML infrastructure, designing reliable real-time data systems, and ensuring models run efficiently and reliably in production.

ROLE RESPONSIBILITIES

• Consult with data scientists on training machine learning models
• Support improvements and additions to the ML infrastructure, including getting your hands dirty with data engineering and DevOps engineering
• Design systems to meet throughput and latency requirements
• Implement NFRs (Non-Functional Requirements) to ensure a high degree of system reliability

THE SKILLS AND EXPERIENCE WE ARE LOOKING FOR

• Prior experience with productionising ML systems is a must.
• Prior experience training machine learning models is highly desirable.
• Advanced knowledge of Python and familiarity with SQL.
• Good working knowledge of Terraform for Infrastructure as Code (IaC)
• A solid understanding and hands-on experience with real-time and event-driven systems such as Kafka, Kafkaconnect, Pub/Sub.
• Solid experience with Kubernetes, docker, deployment types (canary, blue-green etc.)
• Experience with setting up CI/CD systems using tools such as CircleCI, drone, Github actions, ArgoCD.
• Working experience with Big Data technologies such as Spark, Dataflow, and Flink.
• Experience with system design - keeping performance and efficiency in mind, whilst aware of trade-offs.
• Experience applying software engineering rigor to ML, including CI/CD/CT, unit-testing, automation etc.
• Hands-on experience with some MLOps tools such as KubeFlow, DVC, MLFlow.
• Experience with cloud providers, such as GCP, AWS, or Azure
• Prior experience or a strong interest in FinTech space

Please note that all appointments are subject to our background checking process, which may include Credit, Criminal and any other job inherent checks.