Senior Machine Learning Engineer

Lulalend · Cape Town, Western Cape

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What We Do

  • We're Lula. We build innovative fintech products to help SMEs make cash flow. From instant access to funding to all-in-one business banking accounts, we're on it!
  • Our purpose is to help SMEs manage their business better, faster, simpler, Lula, so they can spend more time doing what they love.
  • Speaking of love, we're looking for Lulas who love to make a difference to join our team and change the game.

CULTURE CODE

  • We Embrace Curiosity - We continuously seek better ways to deliver value with a solutions-over-problems mindset.
  • We win as One - We collaborate, build strong relationships and value diverse perspectives
  • We're Driven by Purpose - We are passionate and committed to delivering the best products and services for SMEs
  • We Execute with Ambition - We set ambitious goals, embrace challenges, and deliver with focus and determination.

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.

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
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