Build and maintain integrations between internal systems, data environments, and third-party platforms (HubSpot, Workday, etc.)
Develop automation workflows using low-code/no-code tools where appropriate, and custom code where necessary
Design and implement data pipelines that feed automations with clean, structured inputs
Manage authentication, permissions, and API credentials across integrated systems
Establish and maintain standards for how automations are built, tested, documented, and monitored, including security and governance
Ensure automations are resilient to edge cases, data inconsistencies, and system downtime
Review and refactor existing automations as the portfolio of tools grows
Build and integrate AI-powered workflows including LLM-based processing, classification, and generation steps
Evaluate and integrate emerging automation and AI tooling appropriate to the company's stack
Support the Vibe Coding infrastructure used by the broader AI Builder community internally
Document technical implementations in a way that a future engineer (or a curious Strategist) can understand
Identify when a problem requires broader engineering resources and escalate appropriately

3+ years in software engineering, systems integration, technical operations, or a related role
Proficiency in at least one scripting or programming language (Python strongly preferred; JavaScript/TypeScript a close second)
Experience working with REST APIs, webhooks, and authentication patterns (OAuth, API keys)
Comfort with data: SQL or equivalent, JSON/CSV manipulation, basic data transformation
Experience with at least one automation platform (Zapier, Make, n8n, Temporal, Airflow, or similar)
Ability to read and understand a business process spec and ask the right clarifying questions
Experience with AI/LLM APIs (OpenAI, Anthropic, etc.) and prompt engineering in code
Direct experience integrating with HubSpot, Workday, or similar enterprise SaaS platforms
Exposure to agent frameworks (LangChain, CrewAI, or similar)
Experience with cloud environments (AWS, GCP, or Azure) and serverless patterns
Familiarity with data warehousing or BI tools relevant to the company's stack
Background working in a services business or with bespoke client environments

AI Automation Engineer

Key skills