Applied AI Enginner

International Rescue Committee•Nairobi, NY

1d•$88,277 - $103,222•Remote

About The Position

The International Rescue Committee (IRC) responds to the world's worst humanitarian crises, helping to restore health, safety, education, economic wellbeing, and power to people devastated by conflict and disaster. Founded in 1933 at the call of Albert Einstein, the IRC is one of the world's largest international humanitarian non-governmental organizations (INGO), at work in more than 40 countries and 29 U.S. cities helping people to survive, reclaim control of their future and strengthen their communities. A force for humanity, IRC employees deliver lasting impact by restoring safety, dignity and hope to millions. If you're a solutions-driven, passionate change-maker, come join us in positively impacting the lives of millions of people world-wide for a better future. The International Rescue Committee (IRC) responds to the world's worst humanitarian crises, helping to restore health, safety, education, economic wellbeing, and power to people devastated by conflict and disaster. Founded in 1933 at the call of Albert Einstein, the IRC is one of the world's largest international humanitarian non-governmental organizations (INGO), at work in more than 40 countries and 29 U.S. cities helping people to survive, reclaim control of their future and strengthen their communities. If you're a solutions-driven, passionate change-maker, come join us in positively impacting the lives of millions of people worldwide for a better future. Job Overview The Applied AI Engineer (AAE) ensures AI systems work in the real-world contexts they are designed to serve. This works on 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.

Requirements

3–5+ years of experience in software engineering, technical implementation, or related roles
Proficiency in JavaScript/TypeScript and modern web development practices; comfort with Python or similar scripting languages a plus.
Experience deploying and iterating on LLM-based applications in real-world environments
Experience integrating AI systems with third-party platforms and business tools (e.g. CRMs, messaging platforms, APIs), with familiarity with common integration patterns, authentication approaches, and data flow considerations.
Proficiency with AI-assisted development tools (e.g. Cursor, Claude Code) and a clear-eyed understanding of their limitations and failure modes.
Strong problem-solving skills, with the ability to translate real-world challenges into technical solutions
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, including limitations and failure modes
Ability to work across technical and non-technical teams and translate between them
Experience training non-technical users and managing system handovers
Fluency in English required

Nice To Haves

Experience with communications or messaging platforms (e.g., Zendesk, WhatsApp, Telegram) preferred
Experience in humanitarian, development, or nonprofit contexts strongly preferred
Familiarity with East or Central Africa contexts preferred
additional regional language skills preferred

Responsibilities

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
Test AI systems in real-world conditions to identify failure modes before scale
Applies 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
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

Benefits

10 sick days
10 US holidays
20-25 paid time off days depending on role and tenure
medical insurance starting at $163 per month
dental starting at $6.50 per month
vision starting at $5 per month
FSA for healthcare and commuter costs
a 403b retirement savings plans with immediately vested matching
disability & life insurance
an Employee Assistance Program which is available to our staff and their families to support counseling and care in times of crisis and mental health struggles