AI Platform Engineer

The AI Platform Engineer is responsible for maintaining and operating the IRC's enterprise AI environment, managing platform configurations, API access, and integrations between AI tools and IRC systems. This role focuses on technical operations, ensuring system reliability, security, and effective integration with data and operational environments.

Responsibilities

• Serve as primary technical administrator across IRC enterprise AI environments, 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
• Maintain and improve observability across AI systems, tracking uptime, error rates, token consumption, and integration reliability
• Oversee and document configuration changes, environment updates, and deployment procedures
• Support responsible use by flagging anomalous usage patterns and coordinating with InfoSec on policy adherence and access controls
• Coordinate with DevOps, Software Engineering and Data Engineering teams on deployment processes, environment access, and infrastructure dependencies
• Follow change management procedures for configuration changes, environment updates, and integration deployments
• Develop lightweight scripts, connectors, and automations to support AI-assisted workflows primarily in Python and/or JavaScript/TypeScript
• Troubleshoot integration failures, data flow issues, and API connectivity problems
• Collaborate with data engineering on AI/KM pipeline work including vector store ingestion and retrieval configuration
• Contribute to technical design discussions translating operational requirements into implementable solutions
• Maintain technical documentation for all integrations including architecture notes, runbooks, and dependency maps
• Track and report on AI resource utilization identifying opportunities to reduce waste and improve cost efficiency
• Serve as technical point of contact with InfoSec on AI system security, data handling, access controls, and compliance
• Support risk assessments and security reviews for new AI tools or integrations
• Contribute to development of technical SOPs and best-practice guidelines for AI system use
• Act as technical resource for program and operations teams adopting AI tools
• Participate in rollout planning for new AI capabilities providing input on technical feasibility and operational readiness
• Collaborate on onboarding documentation and technical guidance materials for end users
• Contribute to sprint and project planning with estimates on technical effort and dependencies

Requirements

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

Skills

• Enterprise AI platform administration
• API Management
• Access Control
• Environment Configuration
• LLM application infrastructure
• Prompt pipelines
• Model Context Protocol
• Tool-calling integration frameworks
• Vector Databases
• Retrieval-Augmented Generation
• Embedding workflows
• Databricks
• Python Scripting
• JavaScript Scripting
• REST API Integration
• API authentication patterns
• Webhook Integration
• Error management
• Version Control with Git
• Script deployment
• Integration deployment
• Distributed Systems Monitoring
• SaaS platform monitoring
• Alerting setup
• Log review
• Performance diagnosis
• Availability diagnosis
• Usage Monitoring
• Cost Monitoring
• Cloud service monitoring
• Information Security Principles
• Credential Management Systems
• Audit Logging
• Technical Communication
• Cross-Functional Collaboration
• Technical Documentation Production
• Runbook Development
• Standard Operating Procedures Implementation
• Architecture Documentation

Languages

English