AI Platform Engineer

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
  

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