AI Automation Specialist For Pooling

Role Overview

We are looking for an AI Automations Specialist with a strong foundation in software engineering to design, build, and scale our AI-driven internal infrastructure.

In this role, you will go beyond simple scripting to architect and implement robust automation solutions that leverage Generative AI. You will be responsible for developing backend automation tools using Python and Apps Script (JavaScript), integrating custom AI Agents/LLM workflows into our stack, and engineering the Quality Control and Governance frameworks necessary to ensure the integrity of our AI data annotation pipelines.

Technical Responsibilities

• Architect AI Pipelines: Design, code, and deploy scalable automation pipelines utilizing Generative AI technologies and Large Language Models (LLMs).
• Tool & Script Development: Develop internal tools, custom integrations, and scripts using Python and Apps Script (JavaScript) to streamline engineering and business workflows.
• AI Agent Integration: Seamlessly connect and integrate AI Agents, orchestration frameworks, and APIs into existing company platforms and databases.
• AI Governance & Data Engineering: Build and maintain programmatic Quality Control (QC) frameworks to monitor, validate, and govern AI-driven data annotation initiatives.
• System Optimization: Identify performance bottlenecks in manual processes and refactor them into high-performing automated systems.
  
  

• Core Programming: Strong, production-level proficiency in Python and JavaScript (specifically Google Apps Script / Node.js).
• AI & LLM Engineering: Hands-on experience working with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, and frameworks like LangChain, LlamaIndex, or similar AI agent architectures.
• Systems Integration: Solid understanding of RESTful APIs, webhooks, and data serialization (JSON/XML) to connect disparate systems.
• Data & Testing Frameworks: Experience setting up data validation, unit testing, or quality control frameworks for automated datasets and data annotation workflows.
• DevOps Mindset: Familiarity with version control (Git) and deploying/monitoring automated scripts or microservices.