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The African Population and Health Research Center (APHRC) is a premier research-to-policy institution, generating evidence, strengthening research and related capacity in the African research and development ecosystem, and engaging policy to inform action on health and development. APHRC seeks to drive change by developing strong African research leadership and promoting evidence-informed decision-making (EIDM) across sub-Saharan Africa.
APHRC is cognizant of Africa’s rapidly evolving data ecosystem, which presents both opportunities and challenges, particularly in ensuring secure data sharing while safeguarding individual privacy.
APHRC is seeking to recruit a Machine Learning/Data Engineer, s/he will develop and optimize the AI-powered applications, ensuring seamless integration of machine learning models and LLM APIs. The appointment will be for a period of six(6) months.
KEY RESPONSIBILITIES:
The Center operates on the principle that data is a public good and champions the Findable, Accessible, Interoperable, and Reusable (FAIR) principles of data management to facilitate secure, timely, and user-friendly data access and sharing, ensuring it is available to all authorized users. The Machine Learning specialist will bring essential competencies in mobile AI/ML integration, scalable backend systems, and cloud-native DevOps, positioning DSP at the forefront of AI innovation:
- Integrate and deploy AI/ML models and LLMs within mobile and web applications.
- Develop, manage, and optimize Dockerized AI services.
- Ensure high availability, scalability, and security across cloud and on-prem systems.
- Automate and streamline CI/CD pipelines to facilitate seamless updates.
- Implement cost-monitoring and optimization strategies for cloud resources.
- Develop comprehensive disaster recovery plans for critical AI services.
- Maintain secure and efficient API interactions with AI models.
- Monitor, measure, and enhance API latency for real-time responsiveness.
- Document AI workflows and data pipelines to facilitate cross-team collaboration.
Education Qualifications, Experience and Core Competencies
- Bachelor’s degree in computer science, Data Science, or a related field with specialization in AI/ML.
- Two (2) years of experience in DevOps/cloud roles, including at least one (1) year specifically dedicated to AI model deployment.
- Hands-on experience integrating ML models (TensorFlow Lite, ONNX, PyTorch) into mobile applications.
- Proficiency with Large Language Model (LLM) APIs such as OpenAI, DeepSeek, Claude, and Llama, including advanced prompt engineering techniques.
- Experience in building Retrieval-Augmented Generation (RAG) pipelines, fine-tuning, and developing agent-based systems.
- Strong familiarity with AI orchestration frameworks like LangChain, LlamaIndex, or AutoGen.
- Knowledge and practical experience with vector databases (FAISS, Chroma, Pinecone, Weaviate).
- Proficiency in Python data transformation tools, including pandas, NumPy, scikit-learn, and pyCaret.
- Familiarity with ML model deployment tools (MLflow, Seldon Core).
- Expertise with containerization technologies (Docker) and orchestration platforms (Kubernetes—EKS, GKE, AKS).
- Mastery of cloud services on AWS, GCP, Azure, or on-prem equivalents such as OpenStack.
- Strong experience in Infrastructure as Code (IaC) tools like Terraform, Ansible, or Pulumi.
- . Proficient in setting up CI/CD pipelines using GitHub Actions, ArgoCD, or Jenkins.
- Familiarity with monitoring and logging solutions such as Prometheus, Grafana, and ELK Stack.
- Knowledge of model quantization and edge AI for efficient on-device inference.
- Experience with model serving platforms like TensorFlow Serving or Triton Inference Server.
- Competency in database scaling and management, specifically PostgreSQL/MySQL for AI workloads.
- Strong analytical skills to troubleshoot and solve deployment bottlenecks.
- Ability to manage and optimize cloud/on-prem infrastructure costs.
- Security-focused mindset to ensure robust, secure AI application deployments.
- Proven capability in designing scalable and efficient AI architectures.
- Excellent documentation and knowledge-sharing abilities.
- Excellent teamwork and communication skills.
How to apply
Interested candidates are invited to apply through ML Engineer by July 10, 2025. We regret that only short-listed candidates will be contacted and will be required to submit a Police Clearance Certificate at a certain stage of the recruitment process.