Mid National - AI / Machine Learning Specialist/Developer

E4H FED TA: Embedded Technical Support to the Ministry of National Health Services, Regulation & Coordination (M/o NHSR&C)

Programme Overview 

Evidence for Health (E4H) is a Foreign, Commonwealth & Development Office (FCDO)-funded programme aimed at strengthening Pakistan's healthcare system, thereby decreasing the burden of illness and saving lives. E4H provides technical assistance (TA) to the Federal, Khyber Pakhtunkhwa (KP), and Punjab governments, and is being implemented by Palladium along with Oxford Policy Management (OPM).

Through its flexible, embedded, and demand-driven model, E4H supports the government to achieve a resilient health system that is prepared for health emergencies, responsive to the latest evidence, and delivers equitable, quality, and efficient healthcare services. Specifically, E4H delivers TA across three outputs:

Output 1: Strengthened integrated health security, with a focus on preparing and responding to health emergencies, including pandemics.

Output 2: Strengthened evidence-based decision-making to drive health sector performance and accountability.

Output 3: Improved implementation of Universal Health Coverage, with a focus on ending preventable deaths.

Position Summary

The overall goal is to develop a National Digital Health Hub (NDHH) software and implement an integrated, scalable, and interoperable digital health information platform within a six-month timeframe

Specific Objectives

The specific objectives are to:

1. Link the remaining two provinces to NDHH through secure, standards-aligned interoperability mechanisms and validated data pipelines;
2. Operationalise the Practice Hub to deliver AI-enabled advanced analytics and decision support, including explainable insights and scenario-oriented outputs;
3. Strengthen governance, capacity, and sustainability arrangements so that NDHH is routinely usable and maintainable within Government systems (NHDC/NIH and provincial counterparts).

Strategic Approach

Phase II will follow the same strategic logic established in Phase 1. The focus will be on connecting/linking existing systems and datasets, support interoperability, ensure secure and ethical use of health data, strengthen capacity for routine use, and build a flexible platform aligned with global standards that can adapt to future health system needs.

Key strategic principles include:

• Federated interoperability (connect and exchange rather than replace existing provincial systems)
• Governance-led implementation (data sharing and decision rights defined up-front)
• Incremental rollout with verification (pilot-to-scale with measurable acceptance criteria)
• Human-centred adoption (capacity building, usability, and local operational ownership)

Scope of Work

As a part of this TA assignment, the consultant(s) will cover the following scope of work:

1 Governance, Coordination, and Data Sharing

Building on Phase 1 governance structures, the consultant(s) will:

• Strengthen and operationalise national–provincial coordination mechanisms for NDHH implementation (including provincial inclusion and decision pathways).
• Support the completion/update of data sharing instruments (e.g., MoUs/DSA) between M/o NHSR&C, NIH/NHDC and the participating provinces/areas, clarifying roles, responsibilities, and permissible use.
• Establish practical governance for AI-enabled decision support (approval workflow for use-cases, safeguards, and change control).

2 Provincial Onboarding and Integration (Remaining Two Provinces)

• Conduct onboarding the two remaining provinces (systems integration, linkage, APIs, metadata alignment, connectivity, and privacy constraints).
• Implement and validate secure data exchange pipelines (scheduled ingestion, reconciliation, error handling, and alerting), consistent with the Phase 1 approach to backend integration and validation.
• Use and apply existing national metadata packages and indicator mappings to ensure comparability across provinces while retaining provincial context.
• Conduct integration testing and provincial UAT with documented sign-off.

3 Practice Hub Development (AI-enabled Advanced Analytics)

The consultant(s) will design, develop, test, deploy and operationalise the Practice Hub, including AI models configured and executed in the accordance with NDHH requirements, as a controlled decision-support layer that:

• Enables authorised users to query integrated datasets and receive interpretable results (trends, comparisons, alerts, “” what-if” scenarios, briefs and short summaries).
• Connects with the Knowledge Hub,  so results are linked to relevant policies, guidelines, and evidence resources to support learning and consistent decision making..
• Implements ethical and safety AI controls to protect privacy, reduce bias, ,explain how results are generated , track system use, and restrict access based on user role.
• Uses approved AI services through secure APIs (where approved) to support advanced analysis and summarisation, while protecting sensitive information and tracking data sources.

4 Security, Identity, and Access Management

• Ensure role-based access controls and permissions for new provincial users and for Practice Hub functions (including auditability).
• Protect system and data by using encryption, multi-factor authentication, and secure operating practices with national hosting requirements.

5 Testing, Quality Assurance, and Deployment

• Execute a structured test plan: unit, integration, end-to-end, performance/load, security testing, and UAT.
• Conduct UAT in the two newly onboarded provinces and ensure the NDHH is ready to for  routine use.
• Establish monitoring and operational procedures; configure alerts and routine checks (operational sustainability).

6 Documentation, Training, Capacity Building, and Handover

• Produce comprehensive technical documentation (NDHH architecture, APIs, interoperability mechanisms, and end to end data flows), user guides, and training materials and conduct structured training sessions.
• Strengthen NIH/NHDC operational capacity through hands-on administration, supervised operations, and progressive handover.

Focal points nominated by the Director General (Health) and NIH will be actively engaged in the activity. Staff of HPSIU and the Ministry will also be engaged in the activity. 

Sustainability: Capacity Building, Institutionalisation, and/or Transition Planning

Capacity Building: Targeted trainings, on-the-job mentoring, and practical guidance will strengthen NIH/NHDC and provincial capacity to operate, maintain, and routinely use NDHH, including interoperability, analytics, and decision-support functions.

Institutionalisation: NDHH to be embedded within existing government HIS structures through formal governance arrangements, standard operating procedures, data-sharing agreements, and alignment with national data and interoperability standards.

Transition Planning: A phased handover will transfer full operational ownership to NIH/NHDC and provinces, supported by complete documentation, system access transfer, and formal validation of government readiness at the end of Phase II.

Responsibilities

The AI/Machine Learning Specialist/Developer will be responsible for:

• Inception Report & detailed Workplan including integrated Gantt chart
• Provincial onboarding, Governance, Integration and Go-live of the Remaining Two Provinces
• Practice Hub V1.0 (AI-enabled advanced analytics)
• Security Hardening, Documentation, Capacity Building, and Operational Handover
• Final Phase II report

Timeline and Days

The level of effort (LOE) for the role is 30 days from May 2026 – Sep 2026.

Requirement

Technical Expertise

• Minimum 10 years of experience in machine learning and natural language processing.
• Experience with OpenAI, Google AI APIs, and model integration.
• Experience applying AI in healthcare/public health data systems

Competencies

• Innovative Thinking
• Strong algorithmic and coding skills and data literacy