Job Overview
The Applied AI Engineer (AAE) ensures AI systems work in the real-world contexts they are designed to serve. This role involves designing (e.g., building flows and structured agents), deploying, adapting, testing, and validating AI systems in program environments to ensure they are accurate, usable, and effective in practice.
The AAE works directly with country teams, partners, and communities to configure AI systems for local contexts, including language, cultural nuance, and operational constraints. They are responsible for what goes into the system, how it behaves, and whether it delivers meaningful outcomes for end users, making key implementation decisions on system configuration, behavior and deployment approaches based on field conditions and user needs.
Owning deployments across the full lifecycle, from initial configuration through iteration, evaluation and scale, the AAE translates real-world complexity and field insights into concrete system improvements, product decisions and deployment strategies.
Acting as the bridge between technical development and field implementation, the AAE surfaces failure modes early, strengthens system performance and informs the evolution of tools, workflows and infrastructure required for scalable deployment. The role also identifies patterns across implementations and translates them into reusable frameworks and best practices to improve speed, quality, and consistency across programs.
By enabling high-quality deployments at speed, this role ensures that IRC’s investments in AI translate into tangible impact for the people we serve.
Major Responsibilities
AI Deployment, Configuration & Context Adaptation
- Lead end-to-end deployment of AI systems in program contexts, from initial scoping and configuration through live use
- Adapt systems to local environments, including language, cultural context, and operational realities
- Configure prompts, workflows, and AI System behavior, including conversation flows, instructional logic and user experience
- Curate, structure, and validate domain-specific knowledge bases including system memory and personalization strategies to improve relevance and continuity
- Ensure systems reflect real-world humanitarian knowledge that may not exist in training data
- Support multiple concurrent software deployments
Testing, Validation & Continuous Improvement
- Test AI systems in real-world conditions to identify failure modes before scale
- Apply an agile, iterative approach, rapidly building, testing, and refining systems based on real-world feedback, while exercising strong judgment on when to iterate vs. escalate or rethink approach
- Conduct safety validation and red teaming to identify risks, harms, and unintended consequences ensuring responsible and ethical AI deployment in vulnerable contexts
- Troubleshoot technical and operational issues, including in low-connectivity environments
- Collect and interpret user and community feedback on usability, relevance, and performance
- Escalate systemic issues and collaborate with engineering teams on fixes and improvements
- Define and track success metrics to evaluate system performance, user engagement and real-world impact over time
Partnership & Cross-functional Collaboration
- Train field teams on deploying systems and managing structured handovers to ensure adoption and sustained use
- Document deployment processes, configurations, and lessons learned to build institutional knowledge and support replication
- Work closely with AI engineers and technical teams to translate field insights into system improvements
- Collaborate with program teams and partners to align deployments with priorities
- Act as the primary interface between field teams and technical development during deployment
- Support scoping and technical input for proposals and cost recovery opportunities where deployment capacity is a factor
- Ensure solutions are integrated into workflows in ways that are practical, adopted, and sustained
- Contribute to shared learning across deployments to improve future implementation
- Identify patterns across deployments and translate them into reusable frameworks and best practices to improve speed and quality for future implementations
- Contribute to product design and system improvements by translating field insights into system requirements, feature priorities and roadmap input
Key Working Relationships
- Position Reports to: Sr. Deployment Engineer
- Key Relationships: AI/Technology development teams, IT, Data, regional and country program teams, implementing partners, sector specialists
