AI Engineer

Our Client a Leader within the Healthcare industry based in Sandton requires an AI Engineer.

This is a hybrid role.

• Knowledge and understanding of the Data Science Development Cycle: business understanding data profiling feature derivation and selection data modelling model evaluation productionisation monitoring.

• Ability in applying statistical machine learning techniques to predictive modelling problems and translating this into business solutions.
• Ability to clean and unify messy and complex data sets for easy access and analysis. Combining structured and unstructured data.

• Ability to provide detailed explanations (visually and verbally) representing information in the form of a chart diagram picture using tools such as Kibana Tableau Power BI etc.
• Write programming code in python and SQL based on a prepared design.

• Understand leading edge technologies and best practice around Big Data platforms and distributed data processing i.e. Hadoop ecosystem (distributed computational power)-HDFS/Spark/Kafka.
• Ability to conceptualise and frame a problem develop hypothesis and identify objective measures to estimate accuracy of machine learning/statistical processes and perform testing and validation with careful experiments.

• Understanding of data flows ETL and processing of structured and unstructured data within the data architecture.
• Comprehensive solution design based on a good understanding of the Big Data Architecture.

• Proficiency with AI platforms and ecosystems: Mosaic AI Vector Search Model Serving MLflow Unity Catalog Delta Lake Foundation Model APIs.

• Degree (Honours Masters or PHD) in Data Science Statistics Computer Science Engineering Mathematics and / or a combination of these or related fields.
• Relevant data science and engineering certifications such as Python Microsoft AWS Hadoop big data machine learning databricks and similar cloud infrastructure and platforms.

• 710 years in applied machine learning or AI engineering
• strong experience with Databricks MLflow classical ML and LLM operationalization
• Python/SQL; big data systems; cloud production environments.
• Experience with Python/Microsoft ML and tools available within the machine learning ecosystem ( pandas matplotlib SciPy stack) and working in Jupyter notebooks.
• Experience with SQL and working with large-scale data sets.
• Knowledge and practical experience applying machine learning techniques.
• Experience working in agile development teams.
• Experience in operationalising data science solutions or similar product development.
• Experience in a high-scale production environment is critical.
• Deep expertise in AI engineering MLOps and LLMOps best practices.

Apply/send CV to:

Subject Line: AI Engineer