Senior Machine Learning Engineer

Lulalend; Cape Town; On-site; ZAR 600,000 - 900,000; Full time

Job summary

Lula is seeking a skilled ML Engineer to join the data science- engineering frontier in Cape Town. You will build, deploy, and scale ML systems, improving infrastructure and real-time data platforms to keep models running efficiently in production.

You will collaborate with data scientists and DevOps to implement reliable systems, design for performance, and apply rigorous software engineering practices across CI/CD/CT, testing, and automation.

Qualifications

• Productionising ML systems is required.
• Experience training ML models is highly desirable.
• Advanced knowledge of Python and SQL.
• Terraform for Infrastructure as Code (IaC) expertise.
• Hands-on with real-time/event-driven systems (Kafka, Kafka Connect, Pub/Sub).
• Kubernetes and Docker experience; deployment strategies like canary, blue-green.
• CI/CD tools: CircleCI, Drone, GitHub Actions, ArgoCD.
• Big Data tech: Spark, Dataflow, and Flink.
• Strong system design skills focusing on performance and trade-offs.
• Experience applying software engineering rigor to ML (CI/CD/CT, unit testing, automation).
• Hands-on with MLOps tools: Kubeflow, DVC, MLFlow.
• Cloud providers: GCP, AWS, or Azure.
• Interest or experience in FinTech space.

Responsibilities

• Consult with data scientists on training machine learning models
• Support improvements to ML infrastructure, including data engineering and DevOps
• Design systems to meet throughput and latency requirements
• Implement NFRs to ensure a high degree of system reliability

Job description

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