Senior Machine Learning Engineerd at Lulalend

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.

ROLE 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