At a glance
- Closing date
- 24 November 2026 (30 days left)
- Where
- Nairobi, Kenya
- How to apply
- On brightermonday.co.ke, where the employer takes applications.
Job Background / Overview
The IRC is actively investing in enterprise AI to improve the speed, quality, and scale of its programs and operations. As the organization expands its use of AI platforms, including tools from Anthropic, OpenAI, and integrated data environments, the IT department is building dedicated technical capacity to keep these systems running reliably, securely, and effectively.
The IRC is investing in enterprise AI to improve the speed, quality, and scale of its programs and operations. As that investment grows, so does the need for someone to own the technical layer underneath it: the systems, integrations, and infrastructure that make AI tools work at scale.
The AI Platform Engineer is that person. This is a technical operations role, not a user-facing one. This role is responsible for keeping the IRC's enterprise AI environment running managing platform configurations and API access, building and maintaining integrations between AI tools and IRC systems, writing scripts and automations, and serving as the technical point of contact for data engineering and InfoSec. Day-to-day, this person is more likely to be reading logs, troubleshooting a broken pipeline, or pushing a configuration change than answering questions from end users.
The role works in close coordination with the AI Platform Support Director, who owns vendor relationships, licensing, and adoption strategy. This role owns the technical execution underneath that, ensuring the systems are reliable, observable, and properly connected to the rest of the IRC's data and operational environment.
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 system
o
Tracking 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
Job Background / Overview
The IRC is actively investing in enterprise AI to improve the speed, quality, and scale of its programs and operations. As the organization expands its use of AI platforms, including tools from Anthropic, OpenAI, and integrated data environments, the IT department is building dedicated technical capacity to keep these systems running reliably, securely, and effectively.
The IRC is investing in enterprise AI to improve the speed, quality, and scale of its programs and operations. As that investment grows, so does the need for someone to own the technical layer underneath it: the systems, integrations, and infrastructure that make AI tools work at scale.
The AI Platform Engineer is that person. This is a technical operations role, not a user-facing one. This role is responsible for keeping the IRC's enterprise AI environment running managing platform configurations and API access, building and maintaining integrations between AI tools and IRC systems, writing scripts and automations, and serving as the technical point of contact for data engineering and InfoSec. Day-to-day, this person is more likely to be reading logs, troubleshooting a broken pipeline, or pushing a configuration change than answering questions from end users.
The role works in close coordination with the AI Platform Support Director, who owns vendor relationships, licensing, and adoption strategy. This role owns the technical execution underneath that, ensuring the systems are reliable, observable, and properly connected to the rest of the IRC's data and operational environment.
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 system
o
Tracking 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
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