Applied AI Technology Advsior / Specialist

We are looking for a qualified candidate to fill this position. Position Overview:

• The Technology Advisor (or Specialist), Applied AI, plays a pivotal role at the intersection of health & development, machine learning research, product engineering, and client success.

• The role involves refining complex One Health datasets into model‑ready signals, developing prototypes and tools, and translating real‑world insights into platform features that strengthen GGHN’s AI‑enabled systems.

• The Advisor/Specialist maintains tight feedback loops across GGHN teams to ensure that partners and clients derive maximum value from AI‑driven solutions, while upholding responsible, ethical, and inclusive AI practices.

Responsibilties Coordination of Applied AI, Data Engineering, and Digital Innovation Activities:

• Reports to the Organisational Team Lead and serves as the focal point for Applied AI across GGHN.

• Leads the implementation of AI, machine learning, and digital innovation activities, ensuring alignment with GGHN strategy, project goals, and donor expectations.

• Maintains a collaborative practice with internal and external stakeholders involved in data, digital health, and AI-driven program delivery.

• Monitors progress, identifies technical challenges, and implements corrective actions to ensure high‑quality AI and data engineering outputs.

• Ensures required resources—including datasets, compute environments, tools, and documentation—are available for AI development and deployment.

• Represents GGHN at technical AI, digital health, and innovation forums, meetings, and stakeholder engagements.

• Advocates for responsible, ethical, and inclusive AI practices within GGHN and across partner ecosystems.

Mode of Evaluation:

• Quarterly and annual AI/innovation work plans and achievement reports

• >85% of planned AI and digital innovation activities completed with documented outputs

Ensure AI‑Enabled Solutions Are Functional, Scalable, and Available Across All Target Use Cases:

• AI and data engineering pipelines are established, functional, and maintained across all relevant program areas.

• Data sources, datasets, and signals are identified, cleaned, and transformed into model‑ready formats.

• Each AI solution or model has:

• An annually updated technical strategy and implementation plan

• Documentation of data flows, model architecture, and deployment processes

• Standard operating procedures (SOPs), job aids, and user guides

• Engineering and product teams have the necessary resources to build, test, deploy, and monitor AI models.

• AI tools and prototypes are used to generate insigh...