Machine Learning Engineer (Mid)

Spatialedge; South Africa; On-site; ZAR 600,000 - 900,000; Full time

Job summary

Spatialedge looks for a Machine Learning Engineer to design, develop, deploy, and maintain production-ready ML models and pipelines. You will work in cross-functional squads delivering ML use cases that drive business value across customer value management, experience, revenue, fraud detection, and automation.

You will manage productionisation, ensure code hygiene, and align with MLOps standards. The role requires strong Python/Spark skills, Airflow, and collaboration with data scientists and

Qualifications

• Experience building, deploying, and maintaining ML systems in production.
• Proficient in Python and Spark for data processing and model development.
• Experience with Airflow for workflow orchestration and DAG configuration.
• Familiarity with GitLab and repository management practices.
• Knowledge of Hive, MongoDB and Cassandra databases.
• Experience with QA processes, code reviews, and deployment procedures.
• Strong collaboration with cross-functional teams.

Responsibilities

• Manage end-to-end productionisation of machine learning use cases, ensuring code hygiene, feature selection, and deployment readiness.
• Apply and maintain productionisation checklists and other documentation to standardise handover processes from data scientists.
• Ensure code is tested in ML framework environments and structured for production deployment.
• Configure and manage Airflow DAGs to visualise and orchestrate task dependencies.
• Participate in code reviews, QA testing, and deployment walkthroughs with MLOps.
• Support model retraining schedules and automate endpoint updates for real-time scoring use cases.
• Propose and implement process improvements to streamline handovers and reduce delays.
• Promote awareness and best practices for coding, tooling and testing among data scientists.

Skills

Python

Spark

Airflow

GitLab

Data processing

Productionisation

Cross-functional collaboration

Documentation

Education

Honours degree in Computer Science, Engineering, or related field

Tools

Airflow

GitLab

Hive

MongoDB

Cassandra

Job description

At Spatialedge.ai we deliver value to our customers with solutions that simplify complex decisioning using data, ML and AI. As a Machine Learning Engineer (MLE), you will work within cross-functional squads to deliver scalable, production-ready machine learning solutions. Your work will support ML use cases that drive business value across areas such as customer value management, customer experience, revenue generation, fraud detection, and intelligent automation. You will be responsible for designing, developing, deploying, and maintaining ML models and pipelines.

Key Responsibilities

Manage end-to-end productionisation of machine learning use cases, ensuring code hygiene, feature selection, and deployment readiness.

Apply and maintain productionisation checklists and other documentation to standardise handover processes from data scientists.

Ensure code is tested in ML framework environments and structured for production deployment.

Configure and manage Airflow DAGs to visualise and orchestrate task dependencies.

Participate in code reviews, QA testing, and deployment walkthroughs with MLOps.

Support model retraining schedules and automate endpoint updates for real-time scoring use cases.

Propose and implement process improvements to streamline handovers and reduce delays.

Promote awareness and best practices for coding, tooling and testing among data scientists.

Required Skills & Experience

Experience building, deploying, and maintaining machine learning systems in production environments.

Proficiency in Python and Spark for data processing and model development.

Experience with Airflow for workflow orchestration and DAG configuration.

Familiarity with GitLab and repository management practices.

Knowledge of database systems such as Hive, MongoDB and Cassandra.

Experience with QA processes, code reviews, and deployment procedures.

Strong collaboration skills to work with cross-functional teams.

Ability to manage and optimise productionisation workflows and checklists.

Keeping technical docs up to date for smooth handovers.

Preferred Qualifications

Honours degree in Computer Science, Engineering, or a related field; or equivalent professional experience in machine learning engineering.

+-3 years working experience in the ML space.

ML Certification with Google Cloud Platform (GCP), AWS, Azure or equivalent is advantageous.

Exposure to ML frameworks.

Why Join Us?

At Spatialedge, you will have the opportunity to work with a team of experienced professionals and gain invaluable insights into the world of AI and data-driven decision-making. You will be involved in real projects that make a significant impact on our clients' businesses, including renowned companies in South Africa and abroad!