Careers at International Rescue Committee

Major Responsibilities

AI Systems Administration & Operations (40%)

• Serve as primary technical administrator across IRC enterprise AI environments, currently including Anthropic (Claude) and OpenAI platform deployments

• Manage user access, API key governance, workspace configurations, and environment-level settings across AI platforms

• Monitor system health, usage patterns, and API performance across AI tools; triage and resolve operational issues as they arise

• Maintain and improve observability across AI systemsTracking uptime, error rates, token consumption, and integration reliability

• Oversee and document configuration changes, environment updates, and deployment procedures across managed platforms

• Support responsible use by flagging anomalous usage patterns and coordinating with InfoSec on policy adherence and access controls

Integrations & Technical Implementation (35%)

• Coordinate with the DevOps, SW Engineering and Data Engineering team(s) on deployment processes, environment access, and infrastructure dependencies required to build and maintain AI integrations

• Follow established change management procedures for all configuration changes, environment updates, and integration deployments, including documentation, testing, and appropriate approvals before pushing to production

• Develop lightweight scripts, connectors, and automations to support AI-assisted workflows across teams, primarily in Python and/or JavaScript/TypeScript

• Troubleshoot integration failures, data flow issues, and API connectivity problems across the AI ecosystem

• Collaborate with the data engineering team on AI/KM pipeline work, including vector store ingestion, retrieval configuration, and source data connections

• Contribute to technical design discussions with engineering partners, translating operational requirements into implementable solutions

• Maintain technical documentation for all integrations, including architecture notes, runbooks, and dependency maps

Monitoring, Resource Optimization & InfoSec Liaison (15%)

• Track and report on AI resource utilization across platforms, identifying opportunities to reduce waste and improve cost efficiency in coordination with the AI 

• Serve as the technical point of contact with the InfoSec team on matters related to AI system security, data handling, access controls, and compliance requirements

• Support risk assessments and security reviews for new AI tools or integrations by providing accurate technical context on system behavior and data flows

• Contribute to the development of technical SOPs and best-practice guidelines for AI system use, in coordination with the AI Platform Support Director and relevant stakeholders

Stakeholder Support & Collaboration (10%)

• Act as a technical resource for program and operations teams adopting AI tools, including answering implementation questions, supporting troubleshooting, and identifying configuration solutions

• Participate in rollout planning for new AI capabilities, providing grounded input on technical feasibility, integration requirements, and operational readiness

• Collaborate with the AI Platform Support Director on onboarding documentation and technical guidance materials for end users

• Contribute to sprint and project planning with accurate estimates on technical effort and dependencies

Required Experience & Skills

AI & Cloud Platforms

• Hands-on experience administering enterprise AI platforms (Anthropic, OpenAI, Azure OpenAI, or comparable tools), including API management, access controls, and environment configuration

• Familiarity with LLM application infrastructure: prompt pipelines, Model Context Protocol (MCP), other tool-calling integration frameworks, vector databases, retrieval-augmented generation (RAG) patterns, and embedding workflows

• Experience working with Databricks or comparable data/ML platforms is a strong plus

Integration & Development

• Proficiency in Python and/or JavaScript for scripting, automation, and lightweight integration work

• Experience building and maintaining REST API integrations, including authentication patterns, webhook handling, and error management

• Comfort reading and working within existing codebases without requiring significant architectural guidance

• Familiarity with version control (Git) and standard deployment practices for scripts and integrations

Systems Administration & Monitoring

• Experience monitoring distributed systems or SaaS platforms, including setting up alerting, reviewing logs, and diagnosing performance or availability issues

• Familiarity with usage/cost monitoring for cloud or API-based services

• Comfort operating in live production environments where reliability and data integrity are critical

Security & Compliance

• Working knowledge of information security principles as they apply to SaaS and API-based systems: access controls, credential management, data handling, and audit logging

• Ability to engage constructively with InfoSec teams, providing clear technical context to support reviews and risk assessments

Collaboration & Communication

• Ability to communicate technical concepts clearly to non-technical colleagues and program staff

• Experience contributing to cross-functional teams alongside product, engineering, and operations stakeholders

• Strong documentation habits: runbooks, SOPs, architecture notes, and internal guides

go to method of application »

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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.

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

• 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 

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

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 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), 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

• Experience in humanitarian, development, or nonprofit contexts strongly preferred

• Familiarity with East or Central Africa contexts preferred

• Fluency in English required; additional regional language skills preferred