About The Role

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