Machine Learning Software Engineer

Principal Machine Learning Engineer - Johannesburg (Hybrid, Permanent)

A senior, hands-on engineering leadership role within an elite Data & AI environment, leading the design and delivery of complex, real-time, production-scale machine learning systems. The role combines deep technical capability with architectural leadership, strategy input, mentorship, and thought leadership.

Key Technical Requirements

Architecture & Engineering

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Proven experience designing end-to-end ML ecosystems, not just building models
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Strong capability in real-time, event-driven, and streaming architecture
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Deep experience building highly scalable, fault-tolerant production systems
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Strong understanding of data governance, robustness, security, and quality engineering
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Extensive experience working with microservices architectures



Data Streaming & Real-Time Processing

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Expert-level hands-on experience with:

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Kafka
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Flink
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Beam (advantageous)

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Strong understanding of real-time ingestion, processing, and event pipelines



Cloud & Infrastructure

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Significant experience architecting and delivering on multi-cloud platforms

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Primary strength required: AWS
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Plus meaningful experience across GCP and Azure

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Expected depth across services such as:

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Lambda, S3, RDS, DynamoDB, VPC (or equivalents in other clouds)

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Strong containerisation & orchestration capability:

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Kubernetes
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Docker



Programming & Software Engineering

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Strong multi-language engineering background, ideally across:

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Python
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Go
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Java
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C#
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JavaScript (beneficial)

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Strong CI/CD mindset and modern software engineering best practices
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Comfortable building full applications, APIs, services, and ML-supporting infrastructure



Machine Learning & Data

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Proven production experience with:

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Structured and unstructured data
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Real-world deployment of ML solutions (not only experimentation)

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Experience with:

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Jupyter / notebook-driven environments
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ML platform tooling (e.g., SageMaker or equivalent)
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Semi-supervised learning approaches useful but not mandatory

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Exposure to:

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Large Language Models
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Generative AI and emerging applied AI capabilities



Databases & Storage

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Strong across:

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SQL databases (e.g., MS SQL, MySQL)
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NoSQL platforms (e.g., MongoDB)
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Graph databases (e.g., Neo4j)



Ways of Working

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Experience working in modern Agile environments (Scrum / Kanban)