13 days ago
Cape Town, South AfricaSenior
Responsibilities
- Consult with data scientists on training machine learning models.
- Improve and extend ML infrastructure, including hands-on data engineering and DevOps engineering.
- Design systems that meet throughput and latency requirements.
- Implement non-functional requirements to ensure system reliability.
- Productionize, deploy, and scale machine learning systems efficiently and reliably.
Requirements
- Prior experience productionizing ML systems is required.
- Prior experience training machine learning models is highly desirable.
- Advanced knowledge of Python and familiarity with SQL.
- Working knowledge of Terraform for Infrastructure as Code.
- Hands-on experience with real-time and event-driven systems such as Kafka, Kafkaconnect, and Pub/Sub.
- Experience with Kubernetes, Docker, and deployment types such as canary and blue-green deployments.
- Experience setting up CI/CD systems using CircleCI, Drone, GitHub Actions, or ArgoCD.
- Experience with big data technologies such as Spark, Dataflow, and Flink.
- Experience with system design, performance, efficiency, and engineering trade-offs.
- Experience applying software engineering rigor to ML, including CI/CD/CT, unit testing, and automation.
- Hands-on experience with MLOps tools such as KubeFlow, DVC, and MLFlow.
- Experience with cloud providers such as GCP, AWS, or Azure.
- Prior experience or a strong interest in the fintech space.
Tech Stack
Apache FlinkApache KafkaApache SparkAWSAzureCircleCIDockerDrone CIDVCGitHub ActionsGoogle Cloud PlatformKubernetesMLflowPythonSQLTerraform
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