Salary: Per Ministry of Finance contract staff salary scale

Terms of employment: Contract

Duration of contract: One year with possibility of extension

Place of work: Ethiopian Public Health Institute, Addis Ababa

Quantity: One

Background

The Ethiopian Public Health Institute (EPHI) is the national public health institute mandated to lead, conduct and coordinate the prevention and control of public health emergencies; strengthen the capacity of health laboratories and provide referral and reference testing services; and undertake research on priority public health and nutrition problems, programmes and strategies. EPHI is also mandated to establish and manage the national health data management and analytics center by gathering health and health-related data from multiple sources. The National Data Management and Analytics Center for Health (NDMC) manages national health data and applies advanced analytics, statistical modelling, machine learning, visualization and evidence synthesis to generate timely, policy-relevant evidence for decision-making at national and subnational levels.

With support from the Gates Foundation, EPHI is implementing the project “Strengthening Modeling Capacity to Inform Ethiopian Health Policies,” which supports the development and application of advanced analytical and modelling methods for priority public health questions. The project focuses on producing policy-relevant evidence, establishing reproducible analytical workflows, strengthening the skills of EPHI staff and improving collaboration with the Ministry of Health and public health programmes. The position will contribute to these objectives through its assigned technical responsibilities and capacity-strengthening activities.

Purpose of the Position

The position will develop, evaluate, deploy and maintain secure, reliable and scalable artificial intelligence (AI) and machine-learning solutions for priority public-health and health-system needs. It will cover authorized electronic medical record and other clinical, laboratory, surveillance, routine health, geospatial, text, social-media, audio, image, video and time-series data, and strengthen EPHI capacity to operate these systems responsibly.

Requirements, Architecture and Health Data Engineering

1. Translate priority public-health and clinical use cases into technical requirements for users, decisions, data, validation, interoperability, performance, security, deployment and maintenance with EPHI, Ministry of Health and programme teams.

2. Build documented Python pipelines for authorized, de-identified individual-level Electronic Medical Record (EMR) and Electronic Community Health Information System (eCHIS) data, and for clinical, laboratory, pharmacy, referral, imaging, surveillance, aggregate routine, text, social-media, audio, video, geospatial and time-series data; apply quality checks, metadata, lineage and access controls.

3. Design maintainable architectures and interfaces integrating AI services with EPHI and health-system platforms, including EMR, eCHIS and District Health Information Software 2 (DHIS2), across appropriate batch, streaming or real-time workflows.

3.2 Advanced Artificial Intelligence Development and Evaluation

4. Develop, adapt, fine-tune and evaluate models against transparent baselines using appropriate recent advances, including transformers, foundation and multimodal models, self-supervised learning, transfer learning and parameter-efficient fine-tuning.

5. Develop natural language processing (NLP), large language model (LLM) and generative-AI applications for clinical notes, reports, literature, news and social-media data, including extraction, classification, summarization, event detection, semantic search, retrieval-augmented generation (RAG) and bounded agentic workflows with source traceability and human review.

6. Develop computer-vision, speech, audio, video and multimodal systems, including speech recognition, audio classification, medical or public-health image and video analysis, object detection and tracking, vision-language models and multimodal fusion.

7. Develop geospatial, remote-sensing and spatiotemporal AI systems for mapping, anomaly detection and forecasting using appropriate convolutional neural networks (CNNs), vision transformers, graph neural networks (GNNs), long short-term memory (LSTM) networks or the Temporal Fusion Transformer (TFT).

8. Evaluate predictive performance, calibration, temporal and spatial generalizability, robustness, subgroup fairness, factuality, traceability, privacy risk, latency, cost and usefulness in the intended health-system workflow.

3.3 Production Engineering and Machine-Learning Operations

9. Establish machine-learning operations (MLOps) and generative-AI operations for reproducible data preparation, training, evaluation, experiment tracking, versioning, model registration, controlled release and retraining.

10. Deploy validated models as secure batch or streaming services, applications, dashboards or application programming interfaces (APIs), coordinating integration and operational support with relevant teams.

11. Implement automated testing and continuous integration and continuous delivery (CI/CD) for data, code, models, prompts and interfaces, with safety and failure-mode testing proportionate to risk.

12. Monitor predictive and operational performance, calibration, drift, factuality, bias, security and reliability; establish alerts, incident response, rollback, periodic re-evaluation and retraining criteria.

13. Optimize computing and storage, including graphics processing units (GPUs), across secure on-premises, cloud or edge environments for latency, cost, availability, connectivity and maintainability.

3.4 Governance, Documentation and Capacity Strengthening

14. Apply EPHI requirements for access, confidentiality, cybersecurity, privacy, ethics, fairness, transparency and human oversight, with safeguards for clinical, social-media and audiovisual data and defined clinical accountability.

15. Maintain architecture, data, model, prompt, validation, deployment and decision documentation, including model cards, runbooks and user guidance.

16. Mentor EPHI staff through hands-on projects and structured training in AI development, evaluation, deployment, MLOps, monitoring and responsible AI.

17. Collaborate with clinicians, epidemiologists, geospatial analysts, statisticians, modellers, data engineers, developers, programme experts and policy stakeholders; review AI models, systems and code.

18. Contribute to work planning, progress reporting, partner coordination and other technically relevant assignments agreed with the supervisor.

Reporting

• Reports to: Project Principal Investigator (PI) / Director of the National Data Management and Analytics Center (NDMC) / Head of the Data Analytics, Modelling and Visualization Division.
• The position will be based at EPHI/NDMC in Addis Ababa.

Qualifications and Experience

• Doctor of Philosophy (PhD) in statistics, biostatistics, artificial intelligence, data science, computer science, software engineering, health informatics, biomedical informatics, clinical informatics or a closely related quantitative or computing field, with at least two years of relevant experience; or
• Master’s degree in one of the above fields, with at least six years of relevant experience.
• Demonstrated experience developing, evaluating, deploying and maintaining production AI or machine-learning systems.

Desired Skills and Experience

• Advanced Python programming is required; proficiency in R is preferred, with strong Structured Query Language (SQL) skills.
• Strong experience with established machine-learning and deep-learning frameworks such as PyTorch, TensorFlow and scikit-learn, together with production-quality software-engineering and testing practices.
• Demonstrated experience in at least two AI application areas: NLP or generative AI, computer vision, speech or audio, video, multimodal or spatial AI, or time-series and spatiotemporal modelling.
• Experience with complex health data, including EMR, eCHIS, DHIS2, laboratory, pharmacy, referral, surveillance, imaging, geospatial, social-media, audiovisual or streaming data.
• Experience evaluating transformer, foundation or vision-language models; self-supervised or transfer learning; parameter-efficient fine-tuning; RAG; or bounded agentic workflows.
• Experience deploying containerized AI services or APIs using Docker, Kubernetes or comparable platforms, with Git, CI/CD, model and prompt versioning, registries, automated testing, observability and drift detection.
• Experience with databases, distributed or streaming data processing, secure data platforms and scalable on-premises or cloud computing.
• Knowledge of cybersecurity, de-identification, privacy, fairness, explainability and human oversight for sensitive clinical, public-health, social-media and audiovisual data.
• Experience integrating routine health-information platforms or using Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) is an advantage.
• Evidence of deployed AI systems, maintained software, open-source contributions, technical documentation or relevant peer-reviewed publications.
• Strong analytical, problem-solving, technical-writing, communication and mentoring skills, with the ability to work independently and collaboratively and manage multiple tasks within agreed timelines.

Contract Duration and Remuneration

• The initial contract will be for one year, with possible extension based on satisfactory performance, project requirements and availability of funds.
• Salary will be determined in accordance with the applicable Ethiopian Public Health Institute and Ministry of Finance contract staff salary scale, taking into account the candidate’s qualifications and experience.