About this role
Discovery Health Junior Developer – AI/ML Engineering About Discovery Discovery's core purpose is to enhance and protect people's lives through products and services that use incentives, behavioural science, and data to encourage healthier choices. This creates better outcomes for members, lower claims, and shared value for clients and society. Discovery has one of the most diverse data assets in financial services and healthcare in the world, spanning health, wellness, lifestyle, driving, investments, and life insurance. This creates a unique opportunity to use advanced analytics, causal inference, machine learning, and artificial intelligence to improve member outcomes, strengthen operational performance, and support more personalised products and services.
About the Data Science Lab The Data Science Lab (DS Lab) is a specialist team that solves high-impact problems across Discovery Health and the broader Discovery Group. Using large-scale structured and unstructured data, the team develops models, experiments, decision systems, and AI-enabled solutions that create measurable business and member value, like the Personal Health Pathways (PHP) programme. The DS Lab partners with teams across Discovery and organisations such as Google, Quantium Health, and world-renowned tertiary institutions to advance personalisation, causal inference, optimisation, and AI. The team also contributes to the Vitality AI platform, delivering scalable capabilities across Discovery, Vitality UK, USA and international partner markets.
Junior data scientists gain exposure to cutting-edge work, experienced mentors, and the opportunity to contribute to solutions with real-world impact.
Key Purpose The Junior Developer - AI Engineering role plays an important role in supporting the development, deployment, operation, and continuous improvement of AI and machine learning solutions across the Data Science Lab (DS Lab) portfolio. This role is designed for early-career software, data, platform, or AI engineers who are passionate about building production-grade technology and developing expertise in modern AI engineering practices. Working alongside Senior ML & AI Engineers, Data Scientists, Product Teams, and Platform Engineers, the successful candidate will contribute to the engineering work required to move AI solutions from experimentation into reliable and scalable production systems.
The role provides exposure across software engineering, machine learning engineering, cloud platforms, MLOps, Generative AI, data engineering, observability, and AI operations. The successful candidate will gain hands-on experience building deployment pipelines, supporting model-serving infrastructure, integrating AI services, monitoring production systems, and improving engineering processes across multiple AI products. This role is not focused on developing statistical models or conducting research. Instead, it focuses on engineering, automation, operational support, platform development, and production readiness for AI systems. The successful candidate will help contribute to the engineering foundations that enable reliable deployment, operational excellence, observability, and continuous improvement across Discovery's AI ecosystem.
Areas of responsibility may include but are not limited to: AI Platform Engineering and Production Support
- Support the deployment, operation, and maintenance of AI and machine learning solutions across the DS Lab portfolio.
- Contribute to the development of reusable engineering components, deployment frameworks, and operational tooling.
- Assist with troubleshooting production issues, deployment failures, and platform-related incidents.
- Support platform reliability by monitoring system health and escalating issues
- Participate in implementation activities across cloud-native environments.
- Contribute to technical documentation, engineering runbooks, and operational procedures.
Generative AI and AI Application Development
- Support the implementation of Generative AI, Agentic AI, and conversational AI solutions under the guidance of senior engineers.
- Assist in integrating LLM services, retrieval systems, APIs, and AI applications into broader product ecosystems.
- Contribute to prompt management, testing activities, evaluation workflows, and release processes.
- Help implement monitoring and logging capabilities for AI-powered applications.
- Participate in the development of internal tools and services that support AI product delivery.
Observability and Operational Excellence
- Assist with monitoring AI systems and production services using approved observability tools.
- Contribute to dashboard development, alert implementation, logging standards, and monitoring configurations.
- Help investigate operational issues through log analysis, metrics review, and incident troubleshooting.
- Participate in root cause analysis activities and documentation of lessons learned.
- Support operational readiness activities and production support processes.
- Contribute to maintaining service reliability, availability, and operational performance.
Engineering Standards and Quality Practices
- Follow established engineering standards, coding guidelines, deployment processes, and operational controls.
- Participate in code reviews and technical knowledge-sharing activities.
- Contribute to test automation and software quality initiatives.
- Maintain clear technical documentation and implementation records.
- Apply software engineering best practices under guidance from senior team members.
- Continuously improve technical skills through learning, experimentation, and practical experience.
Technical Skills Required Proficiency in Python for data analysis, and machine learning. Experience working with SQL, relational databases, and structured data. Strong foundations in statistics, machine learning, experimental design, and model evaluation. Ability to write clear, reproducible, and well-documented analytical code. Foundational understanding of artificial intelligence, machine learning, and model deployment concepts. Ability to write clean, maintainable, well-documented, and reusable code.
Strong analytical and problem-solving skills with the ability to investigate, troubleshoot, and resolve technical issues. Strong communication and collaboration skills with the ability to work effectively within multidisciplinary teams. Demonstrated curiosity, learning agility, and enthusiasm for software engineering, cloud technologies, and AI. Advantageous Exposure to Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, or conversational AI applications.
Experience gained through academic projects, internships, hackathons, open-source contributions, or personal projects involving software engineering, data, machine learning, or AI. Exposure to cloud platforms such as Google Cloud Platform (GCP), Microsoft Azure, or Amazon Web Services (AWS). Exposure to containerisation technologies such as Docker and orchestration platforms such as Kubernetes. Familiarity with CI/CD pipelines, automation tools, and deployment practices.
Exposure to machine learning engineering, MLOps, model serving, or AI infrastructure concepts. Exposure to observability, monitoring, logging, and alerting tools. Familiarity with Linux environments, scripting, and command-line tools. Experience applying data science in healthcare, insurance, behavioural science, or personalisation contexts. Education and Experience Bachelor's or Honours degree in quantitative disciplines such as Computer Science, Data Science, Statistics, Mathematics, Actuarial Science, Operations Research, Industrial Engineering, or Applied Mathematics.
Demonstrated aptitude for quantitative problem-solving through academic achievement, research, projects, competitions, internships, or work experience. Postgraduate study, research experience, or data science competition participation would be advantageous. Equivalent qualifications or alternative pathways will be considered where supported by strong analytical and technical capability. Prior industry experience is advantageous but not required. Personal Attributes and Skills Curious and motivated to use data, statistics, and AI to solve meaningful healthcare and business challenges.
Analytical, hypothesis-driven, and evidence-based. Strong problem-solving and communication skills. Comfortable with ambiguity, continuous learning, and iterative improvement. Able to balance multiple priorities while maintaining a broader business perspective. Collaborative, accountable, and proactive. Aligned with Discovery's values and core purpose. EMPLOYMENT EQUITY The Company’s approved Employment Equity Plan and Targets will be considered as part of the recruitment process. As an Equal Opportunities employer, we actively encourage and welcome people with various disabilities to apply.