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Urgent! Temporary Position- Machine Learning(Ml)/Data Engineer at African Population And Health Research Center (APHRC) Job Opening In Kenya, Kenya – Now Hiring Confidential

Temporary Position Machine Learning(Ml)/Data Engineer at African Population And Health Research Center (APHRC)



Job description

Job Description

The African Population and Health Research Center (APHRC) is leading Africa-based, African-led, international research institution headquartered in Nairobi, Kenya, and conducting policy-relevant research on population, health, education, urbanization and related development issues on the continent.


Temporary Position- Machine Learning(Ml)/Data Engineer

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 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:


Education Qualifications, Experience and Core Competencies

  • 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.

  • Bachelor’s degree in computer science, Data Science, or a related field with specialization in AI/ML.

  • Two years of experience in DevOps/cloud roles, including at least one 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.

  • Required Skill Profession

    Other General



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