Senior Applied AI & Automation Engineer
RYZ Labs • Argentina
Role & seniority: Senior Applied AI & Automation Engineer
Stack/tools: Zapier, n8n, LangChain, Retool, Glean, ChatGPT, Claude, AI agents, LLMs, internal APIs; event-driven workflows; enterprise data sources; monitoring/logging
Top 3 responsibilities
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Design, build, and deploy AI-powered workflows and agents; partner with AI strategy and stakeholders
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Estimate technical lift, dependencies, and tradeoffs; prototype, validate, and harden solutions for production
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Document, enable adoption by non-technical users, train power users, and establish ongoing improvement and support
Must-have skills
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Strong judgment on where AI adds leverage vs deterministic automation; human-in-the-loop decisions
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Hands-on experience engineering end-to-end workflow automation in complex enterprise environments
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Ability to translate business processes into discrete steps, define tests, and measure accuracy, latency, and safety
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Proficiency with Zapier/n8n, LangChain, and integrating with enterprise tools and data sources
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Stakeholder communication, requirement discovery, and documentation
Nice-to-haves
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Experience with Glean, ChatGPT/Claude connectors, and internal APIs
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Observability, error handling, and resilience in production workflows
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Familiarity with prompt templating, modular components, and reusable automation patterns
Location & work type: Remote position only; candidates based in Argentina or Uruguay
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. Show more Show less