United Nations Office on Drugs and Crime Consultant to Conduct Technology Stack Analysis Jobs in Kenya; the Programme targets support to the two following institutions in the justice chain (national partners):
1. The Judiciary.
2. The Office of the Director of Public Prosecutions (ODPP).
3. The Kenyan Probation and Aftercare Service (PACS).
4. The Witness Protection Agency (WPA).
5. The National Council on the Administration of Justice (NCAJ).
6. The Ethics and Anti-Corruption Commission (EACC)
7. The National Police Service.
8. The Kenya Prisons Service.
9. The Directorate of Children’s Services EACC developed a comprehensive automation plan to digitize and modernize its case management system.
There are seven key law enforcement processes currently under automation. The goal is to enhance efficiency, transparency, and scalability in handling corruption-related cases while leveraging cutting-edge technologies, including Artificial Intelligence (AI). To achieve this, EACC requires a comprehensive analysis of the technology stack that will underpin the proposed system, with a specific focus on integrating AI capabilities. The analysis will ensure the selection of a robust, secure, and future-proof technology stack that aligns with EACC's operational requirements, regulatory standards, and long-term digital transformation goals.
3. Specific Tasks to be performed by the consultant:
a) Document the system requirements in close consultation with EACC
b) Conduct a comprehensive requirement analysis of the iCMS - Conduct stakeholder engagement to understand EACC's operational needs, challenges, and expectations for the case management system. - Identify functional requirements (e.g., case tracking, document management, reporting, AI-driven analytics) and non-functional requirements (e.g., scalability, security, performance).
c) Undertaking a review of the technology landscape - Analyze current trends and best practices in case management systems, including cloud-based solutions, open-source platforms, proprietary software, and AI-driven tools.
- Evaluate technologies across the following layers:
1. Front-end: User interface frameworks (e.g., React, Angular) and AI-powered user experience enhancements (e.g., chatbots, voice assistants).
2. Back-end: Server-side technologies (e.g., Node.js, Django, Spring Boot) and AI integration frameworks (e.g., TensorFlow, PyTorch).
3. Database: Relational and non-relational databases (e.g., PostgreSQL, MongoDB) with AI-driven data analytics capabilities.
4. Integration: APIs, middleware, and microservices architecture, including AI model deployment and management.
5. Security: Authentication, encryption, and compliance with data protection regulations, with a focus on securing AI models and data.
6. Hosting: Cloud platforms (e.g., AWS, Azure, Google Cloud) vs. on-premises solutions, including AI-specific services (e.g., AWS SageMaker, Azure AI).
d) Identify potential AI use cases for the case management system as well as assess the feasibility, benefits and
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