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

Malaria Consortium is seeking a cross-platform mobile application development consultant in Abuja for a hybrid assignment to deliver a secure, multilingual, offline-capable learning and assessment platform with an AI-powered assistant.

Key responsibilities

• Design and deploy Android and iOS applications, backend APIs, administrator portals, data storage and cloud services.
• Deliver role-based learning paths, content management, assessments, remediation and verifiable certificates.
• Provide English, Hausa and Fulfulde localisation, consent management and low-bandwidth offline learning with secure synchronisation.
• Build a retrieval-augmented AI assistant grounded in approved programme manuals, FAQs and SOPs, with citations, escalation and governance controls.
• Provide programme dashboards, analytics, PDF/Excel/CSV exports, filters, notifications and configurable reporting.
• Implement privacy, access controls, encryption, audit trails, security tests, backups, disaster recovery and performance monitoring.
• Complete integration testing, user acceptance testing, production deployment, training and source-code handover.

Requirements

• At least five years of mobile application development and three years of deploying AI-enabled systems.
• Demonstrable delivery of production learning platforms, multilingual systems, offline-first applications and cloud-based enterprise systems.
• Strong capability in Flutter or React Native, Android/iOS, backend APIs and technologies such as Python, .NET, Node.js or Java.
• Practical knowledge of LLMs, RAG, vector databases, security/privacy, analytics and AI governance.
• Health or public-sector digital-system experience is an advantage; Hausa/Fulfulde language technology experience is desirable.
• Ability to work hybrid and travel to implementing states as required.

The indicative schedule is nine weeks, with 18 deliverables totalling 63 working days. These cover inception, solution architecture, UI/UX, specifications, development environments, mobile and backend development, learning management, assessments, offline synchronisation, the AI assistant, dashboards, integration, security testing, user acceptance testing, deployment, training, source-code handover and final support documentation.

Platform and learning requirements

• Provide Android and iOS applications suitable for publication in the Google Play Store and Apple App Store, supported by backend APIs, data services and a web-based management portal.
• Apply role-based registration and authentication. Users should sign in with a phone number or unique ID using a password or PIN; optional OTP and configurable security policies may be supported.
• Present a privacy notice in English, Hausa and Fulfulde covering collected data, purposes, retention, sharing and rights. Record consent status, version, time and language; users declining consent must be prevented from proceeding.
• Allow users to select and change languages, manage translated learning content, and receive AI guidance, notifications and assessments in the selected language with version control.
• Provide role-specific pathways for CDD, OIC, HFW, supervisors and configurable additional cadres. Show assigned paths, progress, assessments, certificates and outstanding learning requirements.
• Configure mandatory and optional modules, prerequisites, estimated and minimum learning times, learning objectives, completion rules and certification requirements.
• Support reading, scrolling, bookmarks, audio/video playback, pause, replay, downloads and low-bandwidth content delivery.
• Track active learning time with start, pause and resume controls; use in-module questions, baseline assessments, configurable pass marks, retakes and remediation. Completion must meet minimum time, content-consumption, knowledge-check and assessment conditions.
• Issue downloadable certificates with stored records; QR-code verification and a certificate validation service are recommended.
• Allow offline downloads and assessments, encrypted local progress and results, automatic or manual synchronisation, duplicate prevention, conflict resolution and display of the latest synchronisation time.

AI assistant and approved knowledge management

• Build a secure Retrieval-Augmented Generation (RAG) assistant for conversational questions, learning support and procedural guidance, using only approved SARMAAN II programme knowledge such as ePlaybooks, SOPs, manuals, FAQs and authorised documents.
• Ground answers in approved sources and provide document, section and source references. Do not use unrestricted internet content or invent unsupported responses.
• Provide context-aware help based on the learner’s current module, including simpler explanations, responses in English, Hausa or Fulfulde, and appropriate text and audio output.
• Support multilingual voice queries and audio-based assessment interactions to assist users with limited literacy, subject to the agreed technical design.
• Set configurable question limits, restricted topics and assistant behaviour; escalate low-confidence or unresolved questions to a trainer or supervisor instead of fabricating an answer.
• Allow users to rate answers; provide feedback and knowledge-gap analysis. Log questions, responses, feedback, escalations and unanswered questions under agreed privacy and retention controls.
• Give authorised administrators approval, publication, archival and version-control workflows for AI sources. Index only approved versions and monitor AI quality and usage.

Management, analytics and security

• Provide least-privilege user administration for learners, trainers, supervisors, administrators and management, including account creation, updates, deactivation and role assignment.
• Report training participation and outcomes for CDD, OIC, HFW and supervisors. Include state, LGA, facility and role filters, completion and pass/retention rates, learning time, overdue work and assessment performance.
• Identify unusually rapid completion or repeated suspicious behaviour and generate alerts. Supply state, LGA and facility map/chart views and insights into weak learning content.
• Include a version-controlled content management system for modules, FAQs, SOPs, documents and AI sources with create, edit, review, approve, publish and archive processes.
• Support question banks and approval workflows for multiple-choice, true/false, matching and scenario-based assessments, with configurable difficulty and test settings.
• Offer PDF, Excel and CSV exports and scheduled reports filtered by date, state, LGA, role, module and learning pathway; document indicator calculations.
• Provide multilingual push notifications, reminders, announcements, assessment notices and retention-assessment reminders.
• Use encrypted data at rest and in transit, secure APIs and offline storage, role controls, audit logging, session controls and MFA for management users.
• Implement uptime, error, API and storage monitoring, scheduled backups, recovery tests and a disaster-recovery plan with stated recovery point and recovery time objectives (RPO/RTO).

Required deliverables — 63 working days

1. D1: Inception and requirements baseline (3 days). Confirm users, workflows, learning paths, dependencies, traceability, risks, acceptance criteria and an agreed work plan.
2. D2: Architecture and technical design (5 days). Supply system architecture, stack, data model, privacy/security approach, environment and deployment plans, scalability assumptions and recurring cost estimates.
3. D3: UI/UX prototypes (5 days). Present mobile and web user journeys and clickable designs, incorporating representative-user feedback.
4. D4: Specifications and test plan (5 days). Document functional and non-functional specifications, APIs, data dictionary, requirements traceability and QA approach.
5. D5: Development and testing environments (5 days). Configure development/staging systems, repository access, build/deployment pipelines and access controls.
6. D6: Mobile application and backend (5 days). Deliver core mobile, authentication, user roles, dashboards, workflows, database and documented APIs.
7. D7: Learning portal and content management (4 days). Deliver user management, pathways, versioned content workflows, assessments and AI source administration.
8. D8: Assessments and certification (4 days). Deliver question banks, testing rules, attempt records, remediation and verifiable certificates.
9. D9: Offline access and synchronisation (3 days). Deliver encrypted offline content/results, sync-status visibility, conflict/duplicate handling and supporting tests.
10. D10: AI assistant and knowledge base (3 days). Deliver approved-source RAG, references, escalation, feedback, governance, logging and evaluation.
11. D11: Analytics and reporting (3 days). Provide dashboards, indicator definitions, exports, scheduled reports and supporting documentation.
12. D12: Integration and end-to-end testing (4 days). Provide integration evidence, test results, defects, fixes and remaining issue status.
13. D13: Security, privacy, performance and recovery tests (2 days). Document security and concurrency checks, backup/restore tests, recovery and resolution or accepted risks.
14. D14: User acceptance testing and remediation (3 days). Provide UAT plan, representative feedback, remediation, retests and documented sign-off.
15. D15: Production release and readiness (3 days). Deploy the production system with monitoring, recovery procedures, a runbook and go-live checks.
16. D16: Training and knowledge transfer (2 days). Train users, administrators and designated technical staff; provide materials and participation records.
17. D17: Source code and technical handover (2 days). Transfer repositories, deployment assets, database schema, APIs, dependencies/licences and access/ownership.
18. D18: Final reporting, warranty and support (2 days). Summarise acceptance, limitations, costs, outstanding matters, warranty, support and maintenance plans.

The indicative implementation period is nine weeks from contract commencement. Workstreams may overlap, with requirements in week one, architecture and setup in week two, development during weeks three to six, integration/security tests in weeks six to seven, user testing in weeks seven to eight, and deployment, training and handover in week nine.

Technical proposal and delivery conditions

• Describe the proposed minimum viable product and phased approach, technical architecture and technology choices, with a clear dependency-aware plan, milestones and risk register.
• Explain offline and synchronisation design, RAG source governance, AI evaluation and cost controls, privacy, security, data management, disaster recovery and ongoing support.
• Supply examples or references from comparable projects and a transparent financial proposal.
• Identify named personnel, roles, availability and CVs for project management, mobile and backend engineering, AI/RAG, UI/UX, QA and security/data protection. One person may cover more than one role where capacity and separation of duties are demonstrated.
• Keep source code in a repository accessible to the programme owner throughout development. At handover, transfer custom mobile, backend, portal and AI components, configuration, infrastructure scripts, APIs, schemas and agreed operational documentation.
• Disclose open-source and commercial licences, model/provider dependencies and transfer limitations; hand over credentials securely, return programme data in a usable format and follow agreed retention/deletion rules.
• Provide agreed transition support and full architectural knowledge transfer to designated programme representatives.

Acceptance criteria

• All agreed functional requirements, Android and iOS store deployments, multilingual learning, offline operation, AI assistant, dashboards and reports must be operational.
• Security testing and user acceptance testing must be completed with documented sign-off; final documentation, training and knowledge transfer must be delivered.