Senior Machine Learning Engineer (Fintech)

TL;DR

Senior Machine Learning Engineer (Fintech): Building, deploying, and scaling machine learning systems within a high-growth fintech environment with an accent on ML infrastructure, real-time data systems, and model productionization. Focus on designing reliable systems to meet throughput and latency requirements, while applying software engineering rigor to ML pipelines.

Location: Must be based in Cape Town, South Africa

Company

hirify.global is a fintech company focused on providing innovative financial solutions.

What you will do

• Consult with data scientists on the training and lifecycle management of machine learning models.
• Improve and expand ML infrastructure using data engineering and DevOps practices.
• Design systems that meet specific throughput and latency requirements.
• Implement non-functional requirements to ensure high system reliability and performance.
• Manage deployment strategies including canary and blue-green releases.
• Apply software engineering rigor including CI/CD/CT, unit testing, and automation to ML workflows.

Requirements

• Location: Must be based in Cape Town, South Africa
• Proven experience in productionizing machine learning systems.
• Advanced knowledge of Python and familiarity with SQL.
• Hands-on experience with Infrastructure as Code using Terraform.
• Solid understanding of real-time and event-driven systems like Kafka.
• Experience with Kubernetes, Docker, and CI/CD tools like Github Actions or ArgoCD.
• Proficiency with cloud providers (GCP, AWS, or Azure).

Nice to have

• Prior experience training machine learning models.
• Hands-on experience with MLOps tools such as KubeFlow, DVC, or MLFlow.
• Experience with Big Data technologies including Spark, Dataflow, or Flink.
• Previous experience or interest in the FinTech industry.

Culture & Benefits

• Focus on high-reliability engineering and system design excellence.
• Exposure to cutting-edge MLOps and Big Data architecture.
• Opportunity to influence production-grade ML infrastructure at scale.
• Professional environment with a focus on rigorous testing and automation practices.