Cross-Platform Mobile Application Developer (Consultant)

Cross-Platform Mobile Application Developer (Consultant) - Hybrid at Malaria Consortium

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Oct 30, 2026, 6:01 AM

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Established in 2003, Malaria Consortium is one of the world’s leading non-profit organisations specialising in the prevention, control and treatment of malaria and other communicable diseases among vulnerable populations. Our mission is to save lives and improve health in Africa and Asia, through evidence-based programmes that combat targeted diseases and promote universal health coverage.

We are recruiting to fill the position below:

Job Title: Cross-Platform Mobile Application Developer (Consultant)

Location: Abuja (FCT)

Employment Type: Hybrid

Objective of the Assignment

The overall objective is to engage a highly experienced cross-platform mobile application consultant with demonstrated expertise in Artificial Intelligence integration to:

Provide comprehensive management and administrative portals.

Design and develop a secure mobile learning platform for Android and iOS.

Develop an AI-powered assistant based on approved SARMAAN II knowledge sources.

Deploy, train administrators, and provide post-implementation support.

Enable offline learning and synchronization.

Deliver scalable analytics and reporting capabilities.

Ensure compliance with data privacy and security standards.

Build multilingual training and assessment capabilities.

Scope of Work

These should include API services, authentication services, learning management services, AI services, data storage, an analytics engine, a notification engine, Retrieval-Augmented Generation (RAG), knowledge indexing, an AI assistant, AI governance controls, AI analytics and monitoring, and translation support.

The platform architecture should incorporate services and functionalities for user administration, content management, assessment configuration, AI governance, reporting, and monitoring.

The application must support Android and iOS devices, low-bandwidth environments, offline-first architecture, and synchronization services to ensure seamless access and data synchronization.

The selected developer is responsible for the design, development, and deploying a platform architecture for an application to be hosted on the Google Play Store and Apple App Store.

Deliverables

Ref.

Deliverable

Number of days

Minimum Contents

D1

Inception report and requirements baseline

3

Confirmed user groups, workflows, learning pathways, requirements traceability matrix, dependencies, risk assessment, integration assessment, acceptance criteria and approved work plan.

D2

Solution architecture and technical design

5

Architecture diagrams, technology stack, data model, security/privacy design, environment plan, deployment approach, scalability assumptions and recurring cost estimates.

D3

UI/UX designs and prototypes

5

Reviewed user journeys and clickable prototypes for mobile and web interfaces, including feedback from representative users.

D4

Functional/technical specifications and test plan

5

Functional and non-functional specifications, API documentation, data dictionary, requirements traceability and quality assurance/test plan.

D5

Development and test environments

5

Configured development and test/staging environments, repository access, build/deployment process and access controls.

D6

Core mobile application and backend services

5

Working mobile application, authentication, user roles, learning dashboard, core workflows, backend, database and documented APIs.

D7

Learning management portal and content management system

4

User administration, content workflows, learning pathways, content versioning, assessment configuration, and AI knowledge-source administration.

D8

Assessment and certification functionality

4

Assessment of types, question banks, pass/retake rules, attempt records, remediation pathways and verifiable certificates.

D9

Offline functionality and synchronization

3

Offline content access and supported assessments, secure local storage, progress persistence, sync status, duplicate prevention, conflict handling and test evidence.

D10

AI-powered learning assistant and knowledge base

3

RAG over approved programme sources, source references, uncertainty handling, escalation, feedback, governance controls, logging and evaluation results.

D11

Analytics and reporting

3

Management dashboards, agreed indicators, downloadable data exports, scheduled reports and documented calculation definitions.

D12

System integration and end-to-end test report

4

Integration results, end-to-end test cases, test evidence, defect log and resolution status against approved requirements.

D13

Security, privacy, performance and recovery test reports

2

Security/privacy assessment, performance and concurrency results, backup/restore verification, recovery evidence and resolution or formal risk acceptance of findings.

D14

User acceptance testing and remediation

3

UAT plan, representative-user feedback, issue register, retest results, outstanding limitations and documented UAT sign-off.

D15

Production deployment and operational readiness

3

Production release, go-live checklist, monitoring, backup/recovery procedures, operational runbook and verification of production configuration.

D16

Training and knowledge transfer

2

End-user and administrator training, training materials, attendance/participation records and knowledge-transfer sessions for designated technical staff.

D17

Source-code and technical handover

2

Complete source-code repositories, deployment scripts, infrastructure/configuration assets, database schema, API documentation, dependency/licence inventory and transfer of access/ownership.

D18

Final report, warranty and support plan

2

Final delivery and acceptance status, known limitations, outstanding issues, cost summary, warranty terms, support/escalation arrangements and maintenance plan.

Total Number of Days

63 days

Required Qualifications and Experience

The consultant must demonstrate:

Experience implementing multilingual solutions.

Experience deploying cloud-based enterprise systems.

Minimum of 3 years’ experience deploying AI-enabled solutions and similar applications to be built.

Minimum of 5 years' experience in mobile application development.

Proven experience delivering learning management platforms.

Experience supporting offline-first applications.

Strong experience in:

RESTful APIs

Training and capacity building systems

Cloud platforms (Azure, AWS or GCP)

Flutter or React Native

Android and iOS development

Knowledge management systems

AI governance frameworks

AI/LLM integration

Analytics and dashboard development

Multilingual African language solutions.

Health sector digital platforms

Python, NET, Node.js, Java or equivalent backend technologies

Retrieval Augmented Generation (RAG)

Vector databases

Data security and privacy compliance

Functional Requirements

The system may also optionally support OTP authentication and configurable security policies to enhance security and access management.

Users should be able to log in using their phone number or unique ID and authenticate using a password or PIN.

The system should support user registration and authentication through role-based access control, allowing access to features and functionalities based on assigned user roles.

Privacy and Consent Management:

The privacy notice must be available in English, Hausa, and Fulfulde. The system should record each user’s consent status, consent version, timestamp, and selected language for accountability and compliance purposes.

The application must provide users with access to a privacy notice detailing the data collected, the purpose of data collection, the data retention period, data-sharing arrangements, and users’ rights.

Where a user declines to provide consent, the system must prevent further access and redirect the user to the welcome page.

Multilingual Capability

The platform should provide:

The platform will support English, Hausa, and Fulfulde language selection during onboarding, allow users to change their preferred language at any time through the settings menu, provide translation management functionality, deliver AI responses and notifications in the user's selected language, and maintain content version control across all supported languages to ensure consistency and accuracy.

Learning Management Features

The dashboard should display:

Assigned learning path

Assessments

Certificates

Learning progress

Outstanding learning requirements

Each training module should: Clearly define its objectives, outline the learning requirements and prerequisites, provide an estimated duration for completion, specify the minimum learning time required before a learner can progress or complete the course, and establish completion criteria that learners must meet to successfully finish the module and receive recognition or certification.

The system should support role-based learning pathways for: CDD, OIC, HFW, Supervisors and additional cadres as may be configured

Learning paths should include: Mandatory modules, Optional modules, Assessments and Certification requirements

Learning Content Delivery:

Requirements: Low-bandwidth optimization

Features: Play, Pause and Replay

Features: Scroll, Read, Bookmark, Play , Pause and Replay

Requirements: Optimized streaming, Download capability and Low-bandwidth versions

Learning Integrity Controls

The system should implement:

Module-specific configuration of learning time requirements, track active learner engagement throughout the learning process, provide start, pause, and resume functionality for learning sessions, and enforce minimum learning time thresholds to ensure that learners cannot complete a module until the defined learning duration and engagement requirements have been met.

The system should:

Present assessment questions throughout the learning experience to promote active participation, prevent passive or unattended course completion, capture and measure learner understanding, assess competency against defined learning outcomes, apply configurable pass marks to determine successful completion, and provide flexible, configurable retake options for learners who do not achieve the required score.

The platform should:

Measure baseline knowledge and automatically score responses.

Where learners fail:

Guessing behavior should be minimized.

Reattempt policies must be enforced

Relevant content must be revisited

Module completion should require:

Content consumption completed

Assessment criteria achieved.

Knowledge checks completed

Minimum learning time met

Certificate Management:

Store certificate records.

Allow certificate download

Automatically generate certificates

Recommended features:

QR Code verification

Certificate validation service.

Offline Learning Capability:

Because deployment targets field personnel working in low-connectivity environments, offline capability is mandatory.

The application should:

Enable learners to download approved learning content for offline use, access course materials without an internet connection, complete assessments while offline, and securely store assessment results locally on the device for synchronization with the central system once connectivity is restored.

Offline data will be encrypted to protect sensitive information, secured against unauthorized access through appropriate authentication and device-level safeguards, and managed in a manner that prevents duplicate assessment submissions by validating and synchronizing records before they are submitted to the central system.

The system should:

Allow manual sync

Display last synchronization date/time.

Sync automatically

Resolve conflicts

AI Requirements:

The proposed solution must include advanced AI functionality powered by secure Retrieval-Augmented Generation (RAG).

The mobile application should provide: Conversational question-and-answer functionality, Learning support and Procedure guidance.

Requirements

• No hallucinated content.
• Maximum question size configurable
• Grounded responses only
• The solution will support:
• Voice-based questions and interactions in multiple languages, including English, Hausa, and Fulfulde, leveraging voice recognition capabilities to capture learner responses and provide audio-based assessment questions.
• The system will generate both text and audio responses where appropriate, use clear and simple language to improve understanding, and consistently respect each user's selected language preferences across all learning, assessment, and communication activities.
• This functionality is particularly designed to enhance accessibility and usability for low-literacy users and remote field workers who may have limited reading and writing proficiency.
• RAG Knowledge Base
• Furthermore, the AI will support multilingual interactions and deliver clarifications in the user’s selected language while ensuring that all responses remain aligned with and constrained by approved training materials, thereby preserving the integrity, accuracy, and governance of official learning content.
• The AI will understand the learner’s current training module, deliver module-specific assistance, maintain contextual awareness throughout interactions, identify concepts that learners find challenging, simplify complex topics, and provide additional explanations tailored to the learner’s needs.
• The AI-powered learning assistant will provide document references, section references, and source citations for all guidance and responses, ensuring transparency, traceability, and user trust by clearly identifying the origin of information.
• The AI architecture should include only approved content should be indexed, including:
• ePlaybooks
• Approved programme documentation
• FAQs
• SOPs
• Training manuals
• The AI should never use unrestricted internet content when answering learner questions.
• Where confidence levels are low:
• Escalate to Trainer
• Flag unresolved questions
• Escalate to Supervisor
• The AI should not fabricate information.
• AI Feedback System:
• Learners should be able to rate AI responses.
• Ratings should support:
• Analytics
• Knowledge gap analysis
• Continuous improvement
• Administrators should be able to:
• Manage source documents
• Configure escalation rules.
• Configure AI behavior
• Define restricted topics
• The platform should record:
• Unanswered questions while enforcing privacy controls and retention rules.
• Questions submitted
• Feedback received
• Responses generated
• Escalations raised
• Administrators should be able to:
• Publish content
• Archive content
• Version control is mandatory.
• Upload new content
• The AI must index only approved content.
• Approve content
• Management Portal Requirements
• Provide visibility into:
• Completion rates
• Programme performance
• Pass rates
• Retention rates
• Learning progress.
• The system should include a web-based management portal.
• Capabilities should include:
• Update users
• Assign roles.
• Deactivate users
• Create users
• Roles include:
• The system should implement least-privilege access principles.
• Learner
• Supervisor
• Administrator
• Trainer
• Management
• The management portal should provide:
• Filters should include State, LGA, Facility and Role
• Supervisor performance analytics
• OIC performance analytics
• HFW performance analytics
• Track: Completed training, Pending training and Overdue training.
• CDD performance analytics
• Monitor:
• Module progress
• Time spent
• Completion rates
• Learning trends
• The solution should identify:
• Generate alerts.
• Unusually fast completion
• Repeated suspicious behavior
• Provide:
• LGA-level reporting
• Using map and chart visualizations.
• State-level performance reporting
• Facility-level reporting
• Monitor:
• Failure rates
• To identify weak content areas.
• Completion rates
• Scores
• Content Management System (CMS):
• Learning content
• Documents
• AI knowledge sources.
• FAQs
• SOPs
• Modules
• Capabilities should include:
• Review
• Approve
• Create
• Publish
• Archive
• Version control is mandatory.
• Edit
• Assessment Management:
• Approval workflows.
• Question editing
• Question creation
• Test configuration
• Question types may include:
• True/false
• Scenario-based questions
• Reporting Requirements: PDF , Excel and CSV
• Difficulty levels should be configurable.
• Matching
• Multiple choice
• Filters should include:
• Role
• Support for scheduled automated reporting is required.
• LGA
• State
• Learning path
• Module
• Date
• Notification Management:
• Notifications should support English, Hausa and Fulfulde languages.
• Retention assessment reminders
• Assessment alerts
• Reminders
• Push notifications
• Announcements
• Security Requirements:
• MFA for management users
• Backup and recovery controls.
• Secure authentication
• Data encryption in transit
• Secure APIs
• Data encryption at rest
• Secure offline data storage
• Role-based access control
• Session management
• Audit logging
• Infrastructure, Monitoring and Disaster Recovery:
• The solution must provide:
• Recovery testing
• Uptime monitoring
• Storage monitoring
• Disaster recovery plan.
• Error monitoring
• API monitoring
• Scheduled backups
• Developer should define:
• RTO (Recovery Time Objective).
• RPO (Recovery Point Objective)
• Implementation Timeline:
• Development
• UX/UI design
• Go-live support.
• AI integration
• User acceptance testing
• Requirements validation
• Testing
• Training
• Deployment
• Indicative Timeline:
• The schedule allows related workstreams to proceed in parallel where appropriate, while preserving adequate time for testing, remediation, approval, and handover.
• The selected provider should submit a detailed, dependency-aware implementation work plan covering requirements validation, solution architecture, UI/UX design, mobile and backend development, learning management functionality, offline synchronisation, AI integration, analytics, system integration, quality assurance, security testing, user acceptance testing, deployment, training and technical handover.
• The assignment should be implemented over an indicative period of Nine (9) weeks from the contract commencement date.
• Period
• Phase
• Key outputs
• Week 1
• Inception and requirements validation
• Stakeholder consultations, requirements baseline, integration assessment, risk assessment and acceptance matrix.
• Week 2
• Architecture, UI/UX and environment preparation
• Architecture and technical design, prototypes, specifications, test plan and development/test environments.
• Weeks 3–5
• Core platform and backend development
• Mobile application, backend/API services, authentication, learning workflows, and core data services.
• Weeks 4–5
• Learning management, content, assessment and certification
• Management portal, CMS, learning pathways, assessment configuration, learner progress and certificates.
• Weeks 4–6
• Offline functionality and synchronisation
• Offline learning and assessments, secure local storage, synchronisation, conflict handling, and offline test coverage.
• Weeks 4–6
• AI assistant and approved knowledge base
• RAG implementation, content indexing, citations, escalation, feedback, and AI governance controls.
• Weeks 5–6
• Analytics and reporting
• Dashboards, exports, indicator definitions, and reporting validation.
• Weeks 6–7
• System integration and end-to-end testing
• Integration verification, end-to-end tests, defect tracking, and resolution.
• Weeks 6–7
• Security, privacy, performance and recovery testing
• Security/privacy testing, load/performance testing, backup, and restoration verification.
• Weeks 7–8
• User acceptance testing and remediation
• UAT, issue resolution, retesting and documented sign-off.
• Week 9
• Production deployment and training
• Production release, operational readiness checks, administrator/end-user training and knowledge transfer.
• Week 9
• Final handover and acceptance
• Source code and technical handover, final report, warranty/support plan and formal acceptance.
• Acceptance Criteria:
• Offline learning is operational.
• All functional requirements are implemented.
• Android and iOS applications are operational and running on the Google Play Store and the Apple App Store.
• Knowledge transfer is completed.
• Documentation is submitted.
• UAT sign-off is achieved.
• Security testing is passed.
• AI assistant functions as specified.
• Reporting