Overview
Ensures AI systems work in real-world contexts by designing, deploying, adapting, testing, and validating them in program environments. The role bridges technical development and field implementation to ensure AI delivers meaningful outcomes for end users.
Key Responsibilities
- Lead end-to-end deployment of AI systems in program contexts.
- Adapt systems to local environments, including language, cultural context, and operational realities.
- Configure prompts, workflows, and AI system behavior.
- Curate, structure, and validate domain-specific knowledge bases.
- Test AI systems in real-world conditions to identify failure modes.
- Conduct safety validation and red teaming to identify risks and harms.
- Troubleshoot technical and operational issues.
- Collect and interpret user and community feedback.
- Escalate systemic issues and collaborate with engineering teams.
- Define and track success metrics.
- Train field teams on deploying systems.
- Document deployment processes, configurations, and lessons learned.
- Work closely with AI engineers and technical teams.
- Collaborate with program teams and partners.
- Act as the primary interface between field teams and technical development.
- Support scoping and technical input for proposals.
- Ensure solutions are integrated into workflows.
- Contribute to shared learning across deployments.
- Identify patterns across deployments and translate them into reusable frameworks and best practices.
- Contribute to product design and system improvements.
Required Experience
- 3–5+ years of experience in software engineering, technical implementation, or related roles.
- Experience deploying and iterating on LLM-based applications in real-world environments.
- Experience with communications or messaging platforms (e.g., Zendesk, WhatsApp, Telegram) preferred.
- Experience integrating AI systems with third-party platforms and business tools (e.g. CRMs, messaging platforms, APIs).
- Proficiency with AI-assisted development tools (e.g. Cursor, Claude Code).
- Strong problem-solving skills.
- Experience working in low-resource, high-complexity, or field-based environments.
- Ability to operate in ambiguous environments and define structure where none exists.
- Strong understanding of how AI systems behave in practice.
- Ability to work across technical and non-technical teams and translate between them.
- Experience training non-technical users and managing system handovers.
- Experience in humanitarian, development, or nonprofit contexts strongly preferred.
- Familiarity with East or Central Africa contexts preferred.
