Cross-platform Mobile Application Developer (consultant)
full-time
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:
Design and develop a secure mobile learning platform for Android and iOS.
Build multilingual training and assessment capabilities.
Develop an AI-powered assistant based on approved SARMAAN II knowledge sources.
Enable offline learning and synchronization.
Provide comprehensive management and administrative portals.
Deliver scalable analytics and reporting capabilities.
Ensure compliance with data privacy and security standards.
Deploy, train administrators, and provide post-implementation support.
Scope of Work
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.
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 platform architecture should incorporate services and functionalities for user administration, content management, assessment configuration, AI governance, reporting, and monitoring.
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.
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.
SearchJobs
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:
Minimum of 5 years' experience in mobile application development.
Minimum of 3 years' experience deploying AI-enabled solutions and similar applications to be built.
Proven experience delivering learning management platforms.
Experience implementing multilingual solutions.
Experience supporting offline-first applications.
Experience deploying cloud-based enterprise systems.
Strong experience in:
Flutter or React Native
Android and iOS development
Python, NET, Node.js, Java or equivalent backend technologies
AI/LLM integration
Retrieval Augmented Generation (RAG)
Vector databases
Knowledge management systems
RESTful APIs
Cloud platforms (Azure, AWS or GCP)
Data security and privacy compliance
Analytics and dashboard development
Health sector digital platforms
Training and capacity building systems
AI governance frameworks
Multilingual African language solutions.
Functional Requirements
The system should support user registration and authentication through role-based access control, allowing access to features and functionalities based on assigned user roles.
Users should be able to log in using their phone number or unique ID and authenticate using a password or PIN.
The system may also optionally support OTP authentication and configurable security policies to enhance security and access management.
Privacy and Consent Management:
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.
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.
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
Learning progress
Assessments
Certificates
Outstanding learning requirements
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
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.
Learning Content Delivery:
Features: Scroll, Read, Bookmark, Play , Pause and Replay
Requirements: Low-bandwidth optimization
Features: 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:
Relevant content must be revisited
Reattempt policies must be enforced
Guessing behavior should be minimized.
Module completion should require:
Minimum learning time met
Content consumption completed
Knowledge checks completed
Assessment criteria achieved.
Certificate Management:
Automatically generate certificates
Allow certificate download
Store certificate records.
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:
Sync automatically
Allow manual sync
Resolve conflicts
Display last synchronization date/time.
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:
Maximum question size configurable
Grounded responses only
No hallucinated content.
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.
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.
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.
RAG Knowledge Base
The AI architecture should include only approved content should be indexed, including:
ePlaybooks
SOPs
Training manuals
FAQs
Approved programme documentation
The AI should never use unrestricted internet content when answering learner questions.
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 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.
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.
Where confidence levels are low:
Escalate to Trainer
Escalate to Supervisor
Flag unresolved questions
The AI should not fabricate information.
AI Feedback System:
Learners should be able to rate AI responses.
Ratings should support:
Analytics
Continuous improvement
Knowledge gap analysis
Administrators should be able to:
Manage source documents
Configure AI behavior
Define restricted topics
Configure escalation rules.
The platform should record:
Questions submitted
Responses generated
Feedback received
Escalations raised
Unanswered questions while enforcing privacy controls and retention rules.
Administrators should be able to:
Upload new content
Approve content
Publish content
Archive content
The AI must index only approved content.
Version control is mandatory.
Management Portal Requirements
The system should include a web-based management portal.
Provide visibility into:
Programme performance
Completion rates
Pass rates
Retention rates
Learning progress.
Capabilities should include:
Create users
Update users
Deactivate users
Assign roles.
Roles include:
Learner
Trainer
Supervisor
Administrator
Management
The system should implement least-privilege access principles.
The management portal should provide:
CDD performance analytics
OIC performance analytics
HFW performance analytics
Supervisor performance analytics
Filters should include State, LGA, Facility and Role
Track: Completed training, Pending training and Overdue training.
Monitor:
Time spent
Module progress
Completion rates
Learning trends
The solution should identify:
Unusually fast completion
Repeated suspicious behavior
Generate alerts.
Provide:
State-level performance reporting
LGA-level reporting
Facility-level reporting
Using map and chart visualizations.
Monitor:
Scores
Completion rates
Failure rates
To identify weak content areas.
Content Management System (CMS):
Learning content
Modules
FAQs
SOPs
Documents
AI knowledge sources.
Capabilities should include:
Create
Edit
Review
Approve
Publish
Archive
Version control is mandatory.
Assessment Management:
Question creation
Question editing
Test configuration
Approval workflows.
Question types may include:
Multiple choice
True/false
Matching
Scenario-based questions
Difficulty levels should be configurable.
Reporting Requirements: PDF , Excel and CSV
Filters should include:
Date
State
LGA
Role
Module
Learning path
Support for scheduled automated reporting is required.
Notification Management:
Push notifications
Reminders
Announcements
Assessment alerts
Retention assessment reminders
Notifications should support English, Hausa and Fulfulde languages.
Security Requirements:
Secure authentication
Data encryption at rest
Data encryption in transit
MFA for management users
Secure APIs
Session management
Audit logging
Role-based access control
Secure offline data storage
Backup and recovery controls.
Infrastructure, Monitoring and Disaster Recovery:
The solution must provide:
Uptime monitoring
Error monitoring
API monitoring
Storage monitoring
Scheduled backups
Recovery testing
Disaster recovery plan.
Developer should define:
RPO (Recovery Point Objective)
RTO (Recovery Time Objective).
Implementation Timeline:
Requirements validation
UX/UI design
Development
AI integration
Testing
User acceptance testing
Deployment
Training
Go-live support.
Indicative Timeline:
The assignment should be implemented over an indicative period of Nine (9) weeks from the contract commencement date.
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 schedule allows related workstreams to proceed in parallel where appropriate, while preserving adequate time for testing, remediation, approval, and handover.
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:
All functional requirements are implemented.
Android and iOS applications are operational and running on the Google Play Store and the Apple App Store.
AI assistant functions as specified.
Offline learning is operational.
Multilingual support is functional.
Reporting and dashboards are operational.
Security testing is passed.
UAT sign-off is achieved.
Documentation is submitted.
Knowledge transfer is completed.
Proposal Submission Requirements
At least five years of relevant mobile or enterprise application development experience.
Demonstrated delivery of production learning, training, assessment, or workforce support platforms.
Demonstrated experience with offline-first mobile applications and data synchronisation.
Demonstrated experience integrating AI/LLM functionality; practical RAG implementation experience is required for the AI scope.
Experience with multilingual systems and, preferably, Hausa and Fulfulde language technology or validation.
Experience with cloud-hosted systems, APIs, data security, analytics and operational support.
Experience in health or public sector digital systems is an advantage.
Selected candidates will be required to work hybrid and travel to implementing states where required.
Proposed Consultant:
The proposed consultant would identify named personnel, roles, availability and CVs for the project manager, mobile application lead, backend/API developer, AI/RAG specialist, UI/UX designer, QA/test lead and security/data protection expertise. One person may cover multiple roles where the provider demonstrates sufficient capacity and appropriate separation of duties.
Technical proposal requirements:
Understanding the assignment and proposed MVP/phased delivery approach.
Technical architecture and proposed technology stack, including rationale and dependencies.
Offline strategy, synchronization design, and testing approach.
AI/RAG architecture, source governance, evaluation plan and cost controls.
Security, privacy, data management, and disaster recovery approach.
Work plan, milestones, team composition, risk register and support model.
Examples/references for comparable solutions and a transparent financial proposal.
Intellectual Property, Data Ownership and Handover:
The provider must hand over complete, current source code for all custom-developed components, including mobile applications, backend, portal, integration services and AI/RAG orchestration/configuration.
All code must be maintained in a repository accessible to the designated programme owner throughout development, not provided only at the end.
The provider must disclose open-source and commercial dependencies, licence conditions, model/provider dependencies and any restrictions on transfer or continued use.
The provider must hand over deployment scripts, infrastructure configuration, data schema, migration/backup procedures, API documentation and environment configuration, with secrets transferred through a secure process.
Programme data must be returned or transferred in an agreed usable format at contract end; any copies held by the provider must be handled according to the agreed retention and deletion requirements.
The provider must provide reasonable transition support to a successor team under the agreed contract terms.
The provider would have completed knowledge transfer of the entire system architecture to a representative(s) of the programme.