Full Description

Remote position only for professionals based in Argentina or Uruguay

At Ryz Labs we are hiring a SeniorApplied AI & Automation Engineer to design, build, and deploy AI-powered workflows and agents across one of our client's teams. You will partner closely with the AI strategy team and cross-functional stakeholders to

• Translate prioritized ideas into concrete, testable workflows and agents

• Estimate technical lift and surface tradeoffs for different build options

• Rapidly prototype, validate, and deploy solutions in production

• Document, hand off, and support adoption with non-technical teams

This is a hands-on, execution-focused role for someone who is equally comfortable talking to business stakeholders about their process as they are selecting and applying tools like Zapier, LangChain, n8n, Retool, Glean, ChatGPT, Claude, etc. when they are the right abstraction for the problem.

This role is less about experimenting with AI tools and more about engineering reliable, end-to-end workflow automation in a complex enterprise environment. Success requires strong judgment about where AI adds leverage, where deterministic automation is better, and where humans must stay in the loop.

What You’ll Do Partner with our AI strategy team on discovery and prioritization Estimate technical lift, complexity, and dependencies for each idea. Provide level-of-effort estimates and technical considerations that help our AI strategy team prioritize and sequence builds.

Design and build AI agents and workflows Use LLMs to power reasoning, drafting, and decision-making steps. Use tools like Zapier and n8n to orchestrate event-driven workflows across SaaS tools and internal systems. Use LangChain when multi-step agent patterns or toolchains are needed. Integrate with Glean, ChatGPT connectors, and internal APIs to ground agents in enterprise knowledge and data. Implement robust error handling, logging, and fallback paths for critical flows.

Prototype, test, and harden solutions Build lean MVPs to validate approach and gather early feedback. Define and run test plans covering representative and edge-case scenarios. Measure accuracy, reliability, latency, and safety; iterate until ready for production use. Establish clear rules for when agents act autonomously vs. when they pause for human review.

Document, enable, and hand off Create concise documentation for each solution. Provide practical examples to help non-technical users to adopt. Train power users on how to use and lightly modify solutions. Set expectations for support, enhancements, and escalation

Continuously improve and standardize Monitor usage and performance; identify where workflows are succeeding, failing, or under-used. Capture and codify reusable patterns (prompt templates, Zapier blueprints, LangChain components).

Decompose and clarify business workflows Break down messy, cross-functional processes into discrete steps, decision points, inputs, and outputs. Identify where automation, AI-assisted reasoning, or human judgment is most appropriate. Surface assumptions, edge cases, and failure modes before introducing AI into the workflow. \n

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