Machine Learning Engineer

Communicate Recruitment; Wes-Kaap; Hybrid; ZAR 900,000 - 1,300,000; Full time

Job summary

Communicate Recruitment seeks a hands-on Machine Learning Engineer in Cape Town (hybrid). You will productionize ML models, build APIs and scalable data pipelines, and collaborate with data scientists and engineers to deliver measurable business value.

You will develop document intelligence solutions and real-time scoring workflows, ensuring robust testing, monitoring and retraining as needed while aligning with business goals.

Qualifications

• Solid understanding of machine learning, model development, evaluation and tuning.
• Proven experience deploying ML models through APIs, pipelines, model services or automated scoring workflows
• Strong SQL skills and experience working with large and complex datasets
• Experience with Git, automated testing, code reviews and production release practices
• Ability to troubleshoot data, modelling, pipeline, integration and production issues independently
• Strong communication skills with both technical and non-technical stakeholders
• Degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline - or equivalent practical experience

Responsibilities

• Take machine learning models and data science prototypes into production-ready applications and services
• Build solutions for forecasting, optimisation, classification, pattern recognition, personalisation and automation
• Develop document intelligence solutions using OCR, parsing, classification, information extraction and AI-assisted validation
• Build structured and validated outputs from complex and semi-structured data
• Develop batch, real-time and event-driven scoring solutions through APIs, pipelines and reusable services
• Translate business requirements into clearly defined ML problems, features, target variables and measurable outcomes
• Implement robust engineering practices including CI/CD, automated testing, version control, containerisation and orchestration
• Monitor data quality, model performance, prediction drift, failures, response times and infrastructure costs
• Investigate production issues and manage model retraining, optimisation, replacement or retirement when required
• Work closely with Data Scientists, Software Engineers, Product and business stakeholders to deliver scalable solutions
• Contribute to technical design, code reviews, documentation and best-practice development

Skills

Machine learning

SQL

Git

CI/CD

Communication

Education

CS/DS/Math/Engineering degree

Tools

Snowflake

dbt

XGBoost

LightGBM

Job description

Cape Town, CBD

Hybrid

Are you a Machine Learning Engineer who wants to take models beyond notebooks and prototypes and turn them into reliable, production-ready solutions?

We're looking for a hands-on ML Engineer to help build, deploy and maintain machine learning and AI solutions that improve business decisions, automate processes and deliver measurable value.

What You'll Be Doing

• Take machine learning models and data science prototypes into production-ready applications and services
• Build solutions for forecasting, optimisation, classification, pattern recognition, personalisation and automation
• Develop document intelligence solutions using OCR, parsing, classification, information extraction and AI-assisted validation
• Build structured and validated outputs from complex and semi-structured data
• Develop batch, real-time and event-driven scoring solutions through APIs, pipelines and reusable services
• Translate business requirements into clearly defined ML problems, features, target variables and measurable outcomes
• Implement robust engineering practices including CI/CD, automated testing, version control, containerisation and orchestration
• Monitor data quality, model performance, prediction drift, failures, response times and infrastructure costs
• Investigate production issues and manage model retraining, optimisation, replacement or retirement when required
• Work closely with Data Scientists, Software Engineers, Product and business stakeholders to deliver scalable solutions
• Contribute to technical design, code reviews, documentation and best-practice development

What We're Looking For

• Solid understanding of machine learning, model development, evaluation and tuning
• Proven experience deploying ML models through APIs, pipelines, model services or automated scoring workflows
• Strong SQL skills and experience working with large and complex datasets
• Experience with Git, automated testing, code reviews and production release practices
• Ability to troubleshoot data, modelling, pipeline, integration and production issues independently
• Strong communication skills with both technical and non-technical stakeholders
• Degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline - or equivalent practical experience

Advantageous Experience

Experience in any of the following would be highly beneficial:

• 4+ years within financial services, lending, credit risk, collections, fraud or another regulated environment
• MLOps, model registries and experiment tracking
• Workflow orchestration, containerisation, CI/CD and cloud deployment
• Snowflake, Snowpark or dbt
• Real-time decisioning or scoring systems
• Document processing, OCR and semi-structured data extraction
• Model explainability and champion-challenger testing
• Experience with XGBoost, LightGBM or similar modelling frameworks

Technical Environment

You don't need to know every technology listed. Strong software engineering fundamentals, sound machine-learning judgement and the ability to learn new technologies are more important than expertise in one particular platform.

The Ideal Candidate

You're a delivery-focused engineer who understands machine learning deeply but also cares about how models perform in the real world.

You enjoy solving practical business problems, building reliable systems, taking ownership from experimentation through to production, and connecting technical work to measurable customer, operational and financial outcomes.

If you're passionate about turning data science into production impact, this could be an excellent opportunity.

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