AI & Automation Engineer: Build Trusted Automations

Arrdas; New York (NY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Arrdas is seeking an AI & Automation Engineer to turn ideas into practical, reliable solutions. You will map workflows, build integrations between APIs and data sources, and ensure outputs are validated with human review when appropriate.

You should bring experience with APIs, structured data, and languages like Python or TypeScript, plus an understanding of testing and data privacy. Clear communication helps you explain technical choices and tackle ambiguous problems as part of a collaborative

Qualifications

• Experience building automations and integrations.
• Experience with APIs, structured data, and Python or TypeScript.
• Understanding of testing, data privacy, and AI output limitations.
• Ability to explain technical choices clearly and solve ambiguous problems.

Responsibilities

• Map workflows and identify opportunities for automation.
• Build integrations between APIs, data sources, and AI services.
• Evaluate outputs and add validation, error handling, and human review where needed.
• Document workflows and collaborate with the team to maintain dependable systems.

Skills

APIs

Python

TypeScript

Automation

Integrations

Tools

REST APIs

Job description

At Arrdas, design and technology work together. As our AI & Automation Engineer, you’ll help turnideas into considered, useful work alongside a team that values openness, curiosity, andattention to detail.

What you’ll do

• Map workflows and identify useful opportunities for automation.
• Build integrations between APIs, data sources, and AI services.
• Evaluate outputs and add validation, error handling, and human review where needed.
• Document workflows and collaborate with the team to maintain dependable systems.

What you’ll bring

• Examples of integrations, automations, or AI projects you have built.
• Experience working with APIs, structured data, and a language such as Python or TypeScript.
• An understanding of testing, data privacy, and the limitations of AI outputs.
• The ability to explain technical choices clearly and work through ambiguous problems.