Job Description

We provide meaningful careers that connect people with purpose.

We are united by a common purpose of providing the best and safest care; and by our shared values of Care, Truth, Participation, Compassion and Dignity.

Netcare invites you to be part of our journey.

The AI Engineer is responsible for designing, building, deploying, and maintaining advanced AI and machine-learning systems with a focus on cloud platforms, MLOps, LLMOps, and modern scalable cloud architectures. The role leads the full lifecycle of AI products including solution design, feature engineering, model development, deployment, monitoring, governance, and optimisation across classical ML and LLM solutions.

DISRUPTIVE INNOVATION

• Identify and test innovative data science operationalization techniques that can be utilised in predictive analytics solutions across Netcare.
• Assist with research on trends in operationalizing Data Science, specifically for the application in the healthcare industry.
• Engage with business stakeholders in the discovery process to identify the business problem/opportunity, elicit requirements and discuss the expected outcomes of modelling/solutions.
• Partner with business stakeholders to define approaches to resolving key business problems and focus on the development of new business strategies.
• Assist in developing conceptual designs or models to address business requirements.
• Collaborate with subject matter experts to select the relevant sources of data and understand the business requirements to ensure that the models are delivered in an appropriate manner.
• Partner with the Data Engineering team to obtain internal and external information and manage data utilisation.
• Perform pre-processing of data which includes tasks such as data manipulation, transformation, normalisation, standardisation, visualisation and features engineering.
• Review existing data analytics solutions (code and/or models), measure quality and identify potential improvements
• Use data profiling and visualisation to understand and explain data characteristics that will inform modelling approaches.
• Identify and implement the appropriate data mining/statistics/machine learning and serving techniques.
• Implement predictive models on large datasets (including distributed parallel computation platforms such as spark).
• Perform business modelling that translate decisions and business processes into a computational model.
• Validate and test analysis/models using appropriate techniques (back testing, A/B testing, scenario modelling, etc.).
• Implement models using Netcare standard processes and techniques.
• Monitor and maintain models with specific focus on model performance and the results being fit for purpose.
• Ensure full compliance to statutory regulations, policies, procedures, best practice, and professional standards and is in line with the Netcare strategy.
• Review and update all policies relating to operationalizing data science.
• Communicate findings to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defends recommendations.
• Generate concise reports with relevant visualisations and commentary for management.
• Lead development of scalable AI/ML and LLM pipelines using Databricks, MLflow, Delta Lake and Spark.
• Architect MLOps and LLMOps frameworks including CI/CD, retraining, monitoring and governance.
• Build distributed training, fine‑tuning and inference workloads using GPUs and modern compute frameworks.
• Deploy secure and robust model-serving architectures and APIs.
• Optimize model latency, scalability and reliability in production.
• Evaluate and introduce modern AI techniques such as RAG, transformers, multi-modal models and agents.
• Research and integrate best practices for MLOps, LLMOps and AI engineering.
• Identify opportunities to modernise existing AI solutions.
• Partner with Data Engineering to develop well‑architected data pipelines and feature stores.
• Perform advanced feature engineering on structured and unstructured data.
• Ensure reproducibility, data lineage and metadata management using MLflow.
• Implement responsible AI frameworks including bias detection, fairness and explainability.
• Define and maintain documentation, standards and policies governing the AI lifecycle.
• Collaborate with business and clinical leaders to translate opportunities into engineered AI solutions.
• Provide mentoring and technical leadership to data scientists, analysts, and engineers.
• Communicate risks, model behaviour and trade-offs clearly to stakeholders.
• Translate business use cases into well-defined AI pipeline inputs, outputs, tasks, chains, tools, and multi-stage reasoning flows.
• Design effective prompts, and agent templates to achieve formatted outputs, minimize hallucinations, prevent data leakage, and enforce guardrails/safety.
• Prepare data for RAG: apply chunking/re-chunking strategies (basic + advanced), filter noise, extract content with appropriate Python packages, load chunked data into Delta Lake/Unity Catalog tables, and build high-quality retrieval systems with re-ranking.
• Develop GenAI applications: select and integrate LLMs/embeddings (based on model cards, context length, metrics), create custom tools/chains (including LangChain/pyfunc with pre/post-processing), augment prompts with retrieved context/user intents, and implement LLM guardrails against malicious inputs or negative outcomes.
• Assemble and deploy solutions: register models in MLflow/Unity Catalog, create/query Vector Search indexes, deploy Model Serving endpoints with access controls, serve Foundation Model APIs or batch inference (e.g., ai_query()), and manage dependencies/signatures/input examples.
• Ensure governance and compliance: apply masking, guardrail techniques, evaluate data source licensing/legal risks, and recommend mitigations for problematic content.
• Evaluate and monitor: select models/architectures via quantitative metrics, track performance/costs with MLflow inference logging, inference tables, Agent Monitoring, and Databricks cost controls; compare evaluation vs. monitoring phases and use appropriate judges/metrics for RAG/live endpoints.

EDUCATION

Required

• 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.

WORK EXPERIENCE

Required

• 7–10+ 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 (i.e.numpy, 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.
• Understanding of scalable cloud and distributed data architectures.
• Ability to design robust model-serving infrastructure.
• Strong capability to translate business and clinical problems into AI solutions.
• Familiarity with healthcare data and privacy legislation (advantage).

KNOWLEDGE

Required

• Provide support to the ever-evolving Netcare strategy of person-centred health and care and the digitisation strategy. Continuously deepen the awareness of the strategy to address new challenges within the Healthcare sector, to build a competitive advantage and sustainability through the Netcare moat strategy.
• Knowledge and understanding of the Data Science Development Cycle: business understanding, data profiling, feature derivation and selection, data modelling, model evaluation, productionisation, monitoring.
• Outstanding problem solving and analytical skills.
• Knowledge of how the business functions and the underlying strategy which supports the business model as well as improving clinical health and care.
• The ability to build, analyse and interpret numerical and non-numerical data to determine potential statistical inferences to inform business and clinical decisions.
• 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.
• Strong Business and clinical knowledge that will contribute to exposing patterns and an aptitude to understanding how the business functions and the underlying strategy which supports the business model as well as improving clinical health and care.
• Knowledge of health-related policies, procedures and legislation (advantageous).
• Hands-on experience building and deploying RAG applications, LLM chains, and agentic systems.
• Proficiency with AI platforms and ecosystems: Mosaic AI Vector Search, Model Serving, MLflow, Unity Catalog, Delta Lake, Foundation Model APIs.
• Strong knowledge of LangChain (or similar) for chains/agents/tools, prompt engineering, chunking strategies, retrieval evaluation, and re-ranking.
• Familiarity with model selection (from hubs/marketplaces), embedding context lengths, guardrails, hallucinations mitigation, and safety/quality assessment.
• Python expertise for data extraction, processing, and pipeline orchestration.
• Understanding of GenAI evaluation metrics, monitoring, cost optimization, and governance in production LLM deployments.

Join the team committed to providing the best and safest health and care.