Senior Machine Learning Engineer
Lulalend · Cape Town, Western Cape
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Start free — we apply for you →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