Senior Machine Learning Engineer

Lula; Cape Town; On-site; ZAR 700,000 - 1,100,000; Full time

Job summary

Lula is seeking a data/ML infrastructure engineer to build, deploy, and scale ML systems at the intersection of data science and engineering. You will enhance ML infrastructure, design real-time data systems, and ensure models run reliably in production.

Responsibilities include collaborating with data scientists, implementing NFRs, and delivering scalable pipelines with Kubernetes, Cloud tech, and CI/CD automation. Fintech focus withCape Town-based opportunities.

Qualifications

• Productionising ML systems experience is required.
• Experience training machine learning models is highly desirable.
• Proficient in Python and familiar with SQL.
• Hands-on with real-time/event-driven systems (Kafka, Kafka Connect, Pub/Sub).
• Solid knowledge of Kubernetes and Docker; experience with canary/blue-green deployments.
• CI/CD tooling experience (CircleCI, Drone, GitHub Actions, ArgoCD).
• Experience with Spark, Dataflow or Flink; ML/DVC/MLFlow tooling helps.
• Cloud familiarity (GCP, AWS, or Azure) and interest in FinTech.

Responsibilities

• Consult with data scientists on training ML models.
• Enhance ML infrastructure, including data engineering and DevOps tasks.
• Design systems to meet throughput and latency requirements.
• Implement non-functional requirements for high reliability.

Skills

Python

SQL

Tools

Terraform

Kafka

Kafka Connect

Pub/Sub

Kubernetes

Docker

CI/CD (CircleCI)

Drone

Github Actions

ArgoCD

Spark

Dataflow

Flink

Kubeflow

DVC

MLFlow

GCP

AWS

Azure

Job description

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