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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

*

Proven experience designing end-to-end ML ecosystems, not just building models
*

Strong capability in real-time, event-driven, and streaming architecture
*

Deep experience building highly scalable, fault-tolerant production systems
*

Strong understanding of data governance, robustness, security, and quality engineering
*

Extensive experience working with microservices architectures



Data Streaming & Real-Time Processing

*

Expert-level hands-on experience with:

*

Kafka
*

Flink
*

Beam (advantageous)

*

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



Cloud & Infrastructure

*

Significant experience architecting and delivering on multi-cloud platforms

*

Primary strength required: AWS
*

Plus meaningful experience across GCP and Azure

*

Expected depth across services such as:

*

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

*

Strong containerisation & orchestration capability:

*

Kubernetes
*

Docker



Programming & Software Engineering

*

Strong multi-language engineering background, ideally across:

*

Python
*

Go
*

Java
*

C#
*

JavaScript (beneficial)

*

Strong CI/CD mindset and modern software engineering best practices
*

Comfortable building full applications, APIs, services, and ML-supporting infrastructure



Machine Learning & Data

*

Proven production experience with:

*

Structured and unstructured data
*

Real-world deployment of ML solutions (not only experimentation)

*

Experience with:

*

Jupyter / notebook-driven environments
*

ML platform tooling (e.g., SageMaker or equivalent)
*

Semi-supervised learning approaches useful but not mandatory

*

Exposure to:

*

Large Language Models
*

Generative AI and emerging applied AI capabilities



Databases & Storage

*

Strong across:

*

SQL databases (e.g., MS SQL, MySQL)
*

NoSQL platforms (e.g., MongoDB)
*

Graph databases (e.g., Neo4j)



Ways of Working

*

Experience working in modern Agile environments (Scrum / Kanban)

About the Role

We're seeking an experienced AI Engineer to lead the design and implementation of AI-driven solutions within a complex enterprise environment. This is a strategic role requiring strong technical depth and the ability to influence architecture decisions at an enterprise level. You'll work closely with senior stakeholders to shape AI road maps and deliver impactful solutions.



Key Responsibilities:



*

Consulting & Advisory:

*

Assess and prepare data sources for AI readiness, including APIs.

*

Provide strategic guidance on business process improvement using generative AI.

*

Implementation & Integration:

*

Deploy AI capabilities leveraging Microsoft technologies such as Copilot Studio (Lite & Full) and Azure AI Foundry.

*

Integrate AI solutions into Dynamics 365 (D365) environments.

*

Architecture & Strategy:

*

Lead architecture discussions and create solution roadmaps from scratch.

*

Ensure data readiness and security for AI applications, including cybersecurity considerations.

*

Cross-Platform Expertise:

*

Evaluate and incorporate AI offerings beyond Microsoft's ecosystem (AWS Bedrock, GCP).

*

Explore model concept protocols and multi-cloud strategies



What We're Looking For

*

Solid technical foundation with senior-level C# development experience.

*

Strong understanding of AI architecture, data integration, and Azure AI Foundry.

*

Ability to hold your own in architecture discussions and communicate complex ideas clearly.

*

Experience in enterprise environments, ideally banking or large corporate settings.

*

Familiarity with Dynamics 365, Copilot Studio, and generative AI applications.

*

Bonus: Knowledge of AWS Bedrock, GCP, and data security best practices for AI.



Why This Role?

*

Full-time allocation to a major enterprise AI initiative.

*

Opportunity to influence strategic decisions and work on cutting-edge AI projects.

*

Join a team that's ahead of the curve in implementing AI solutions at scale.

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

*

Proven experience designing end-to-end ML ecosystems, not just building models
*

Strong capability in real-time, event-driven, and streaming architecture
*

Deep experience building highly scalable, fault-tolerant production systems
*

Strong understanding of data governance, robustness, security, and quality engineering
*

Extensive experience working with microservices architectures



Data Streaming & Real-Time Processing

*

Expert-level hands-on experience with:

*

Kafka
*

Flink
*

Beam (advantageous)

*

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



Cloud & Infrastructure

*

Significant experience architecting and delivering on multi-cloud platforms

*

Primary strength required: AWS
*

Plus meaningful experience across GCP and Azure

*

Expected depth across services such as:

*

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

*

Strong containerisation & orchestration capability:

*

Kubernetes
*

Docker



Programming & Software Engineering

*

Strong multi-language engineering background, ideally across:

*

Python
*

Go
*

Java
*

C#
*

JavaScript (beneficial)

*

Strong CI/CD mindset and modern software engineering best practices
*

Comfortable building full applications, APIs, services, and ML-supporting infrastructure



Machine Learning & Data

*

Proven production experience with:

*

Structured and unstructured data
*

Real-world deployment of ML solutions (not only experimentation)

*

Experience with:

*

Jupyter / notebook-driven environments
*

ML platform tooling (e.g., SageMaker or equivalent)
*

Semi-supervised learning approaches useful but not mandatory

*

Exposure to:

*

Large Language Models
*

Generative AI and emerging applied AI capabilities



Databases & Storage

*

Strong across:

*

SQL databases (e.g., MS SQL, MySQL)
*

NoSQL platforms (e.g., MongoDB)
*

Graph databases (e.g., Neo4j)



Ways of Working

*

Experience working in modern Agile environments (Scrum / Kanban)