HealthGrid Africa · AI Engineer for Healthcare
AI Engineering Fellow (Health Domain)
4 months · 15 hrs/week · Remote · Advanced
HealthGrid Africa is a fictional simulated work environment created for the Jay Health Work Experience Programme. It is not a real or verified employer, and this programme is an educational work-experience placement, not employment.
Programme overview
Explore the design of a healthcare AI project through problem definition, data requirements, evaluation planning and safety controls for complex claims use cases.
AI Engineering Fellow (Health Domain) Work Lab
The AI Engineering Fellow (Health Domain) Work Lab programme scope centres on data requirements for complex claims scenarios. Its intended outputs include healthcare ai project specification, data requirements and risk assessment, evaluation and oversight plan.
What you will do: AI Engineering Fellow (Health Domain) Work Lab
- Specify inputs, outputs and assumptions for an AI use case
- Assess data gaps, bias risks and failure scenarios
- Design evaluation criteria and human oversight requirements
Through role-focused educational assignments at HealthGrid Africa, you will work through case briefs, document the evidence behind your decisions and prepare work samples relevant to data requirements for complex claims scenarios.
Work you will prepare for review
- Healthcare AI project specification
- Data requirements and risk assessment
- Evaluation and oversight plan
Methods you will practise
- AI project specification templates
- Evaluation matrices and model documentation guides
Your weekly work cycle
- Develop and revise your healthcare ai project specification
- Submit progress on your data requirements and risk assessment with evidence and outstanding questions
- Discuss data requirements for complex claims scenarios findings in a supervisor review and record agreed next steps
Feedback and improvement. Your AI Engineering Fellow (Health Domain) assignments will be reviewed against the programme’s assessment criteria. You will explain your approach, discuss corrections and refine your deliverables before including permitted samples in your portfolio. The programme runs for 4 months, with an expected commitment of 15 hours per week; agreed assignments and review arrangements are confirmed in your enrolment offer.
Role description
Purpose of the role. The AI Engineering Fellow (Health Domain) programme develops role-specific judgement and professional work samples through data requirements for complex claims scenarios. The emphasis is on explaining your approach, producing clear evidence and improving work in response to supervisor feedback.
Work environment. A remote educational AI Engineering Fellow (Health Domain) programme set in the fictional HealthGrid Africa environment, with role-focused case briefs, structured assignments and supervisor review.
Day-to-day responsibilities
- Specify inputs, outputs and assumptions for an AI use case
- Assess data gaps, bias risks and failure scenarios
- Design evaluation criteria and human oversight requirements
Weekly responsibilities
- Develop and revise your healthcare ai project specification
- Submit progress on your data requirements and risk assessment with evidence and outstanding questions
- Discuss data requirements for complex claims scenarios findings in a supervisor review and record agreed next steps
Expected deliverables
- Healthcare AI project specification
- Data requirements and risk assessment
- Evaluation and oversight plan
Reporting and supervision. The AI Engineering Fellow (Health Domain) programme scope includes an assigned supervisor reviewing role-specific deliverables, explaining corrections and assessing work against the criteria in your enrolment offer.
Tools and methods relevant to this programme
- AI project specification templates
- Evaluation matrices and model documentation guides
Requirements
Qualifications or professional background
- Computer science, data science, engineering or health-informatics background
Required skills
- Analytical thinking
- Data literacy
- Clear technical writing
Preferred skills
- Python or SQL familiarity
- Interest in health insurance data
Previous experience. Some relevant work or placement experience is expected.
Digital literacy. Comfortable using web applications, spreadsheets and online communication tools.
Communication. Clear written English and timely responses to team messages and supervisor feedback.
Availability. 15 hours per week for 4 months, including scheduled reviews.
Technical requirements. A laptop or desktop computer, a modern browser and a stable internet connection.
Professional and ethical boundaries
- Educational casework only; no employment, real patient care, payer transactions or production system access is implied
- Use only fictional or authorised educational material; do not upload real patient or confidential employer information
- Specialist software, live integrations and dedicated role-specific workflows are not included in the current platform
Expected workload
Hours per week. 15 hours.
Assignment scope. Role-specific case briefs and deliverables focused on data requirements for complex claims scenarios. Assignment volume and due dates are confirmed in the enrolment offer within the 15-hour weekly commitment.
Deadlines and reporting. Tasks carry individual due dates; a weekly summary is due at the end of each week.
Meetings. One weekly team stand-up and one supervisor review session.
Attendance. Check in and out of the work lab for each working session.
How performance is evaluated
- Accuracy and relevance of healthcare ai project specification
- Evidence and reasoning in data requirements and risk assessment
- Documentation quality and professional boundaries
- Deadline adherence and response to feedback
Benefits
- Practical work-experience programme
- Access to Remote Career Launchpad
- Free mentor access according to the programme's mentoring arrangements
- Access to the Jay Health job board
- 10% discount on eligible Jay Health courses
- Supervisor feedback
- Opportunity to build a portfolio of permitted work samples
- Performance-based reward eligibility, where applicable (conditional, not guaranteed)
Learning outcomes
By the end of the programme you should be able to:
- Explain the key decisions and standards involved in data requirements for complex claims scenarios
- Prepare professional healthcare ai project specification
- Prepare professional data requirements and risk assessment
- Prepare professional evaluation and oversight plan
- Use supervisor feedback to improve the clarity, accuracy and completeness of your work
Selection process
- Application review
- Interview
- Role-relevant assessment
- Acceptance decision
- Enrolment and payment of the ₦20,000 enrolment fee (only after acceptance)
- Onboarding