Senior AI Automation Engineer

Innovecs; Teletrabalho; BRL 120.000 - 160.000; Tempo integral

Resumo da oferta

A technology firm based in Brazil seeks an experienced Automation Engineer to design AI-powered workflows utilizing low-code platforms. Candidates should have strong JavaScript skills and experience with LLM APIs, REST APIs, and creating automation structures. This role requires collaboration with tech teams, problem-solving capabilities, and a strong understanding of software integration processes. The position demands independence and good communication skills, given the diverse teams across time zones.

Qualificações

• 3+ years of professional experience building integrations, automations, or workflow systems.
• Upper-Intermediate English level to communicate with our teams.
• Experience with REST APIs, webhooks, and authentication patterns.

Responsabilidades

• Design and deploy AI-powered automation workflows using low-code platforms and custom JavaScript.
• Build intelligent systems that leverage large language models (LLMs) for decision-making.
• Monitor automation health, debug failures, and continuously optimize workflows.

Conhecimentos

low-code automation platforms

JavaScript

LLM APIs

problem-solving skills

API documentation reading

Ferramentas

Python

Zapier

AWS

Descrição da oferta de emprego

Requirements

• 3+ years of professional experience building integrations, automations, or workflow systems
• Strong proficiency with low-code / no-code automation platforms (n8n, AI for business automation- Tray.ai, Zapier, Make | AI Workflow Automation Software & Tools ,or similar)
• Solid JavaScript skills -most automation logic happens in JavaScript nodes and custom functions
• Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, or similar) and prompt engineering
• Experience working with REST APIs, webhooks, and authentication patterns (OAuth, API keys, JWT)
• Understanding of data transformation and formats (JSON, XML, CSV)
• Ability to read API documentation, debug integration issues, and troubleshoot automation failures
• Experience building event-driven workflows and handling asynchronous processes
• Strong problem-solving skills and ability to translate business requirements into technical solutions
• Self-motivated, disciplined, and comfortable working independently across time zones
• Great communication skills - ability to explain technical concepts to non-technical stakeholders
• Upper‑Intermediate English level to communicate with our teams
• Preferred Skills / Abilities
• Experience building AI agents or multi‑step LLM workflows (LangChain, CrewAI, AutoGen, or custom implementations)
• Python scripting for data processing, API integration, or ETL workflows
• Familiarity with workflow orchestration tools or iPaaS platforms (Temporal, Workato, MuleSoft)
• Understanding of vector databases and semantic search for RAG implementations
• Experience with freight, logistics, or supply chain systems and terminology
• Knowledge of SQL and database queries for data analysis
• Background in operations, RevOps, data engineering, or technical product roles
• Experience measuring and optimizing automation ROI (cost per call, error rates, processing time)
• Experience with cloud platforms (AWS, Azure or GCP) and serverless architectures
• Understanding of microservices architecture and event‑driven systems

Responsibilities

• Design and deploy AI‑powered automation workflows using low‑code platforms and custom JavaScript
• Build intelligent systems that leverage large language models (LLMs) for decision‑making, data extraction, and process automation
• Create and maintain integrations between internal systems and third‑party platforms using APIs, webhooks, and event‑driven architectures
• Develop data pipelines that transform, enrich, and route information across multiple systems and data sources
• Implement prompt engineering strategies and design multi‑step LLM orchestration workflows
• Build and optimize automated workflows that eliminate repetitive manual tasks across operations teams
• Monitor automation health, debug failures, and continuously optimize workflows for reliability, performance, and cost‑efficiency
• Identify new automation opportunities by collaborating directly with operations, product, and engineering teams
• Document workflows, create runbooks, and establish best practices for scalable AI automation
• Participate in code reviews and contribute to shared automation libraries, frameworks, and templates
• Measure and report on automation performance metrics, including accuracy, latency, cost savings, and operational impact
• Prototype new AI capabilities and experiment with emerging technologies as part of our Technical Innovation Program

Example Projects & Initiatives

Here are examples of projects our AI Automation Engineers work on :

Intelligent Document Processing

• Build systems that automatically extract structured data from unstructured documents (PDFs, emails, images)
• Create validation workflows that use AI to verify data accuracy and flag anomalies for human review
• Design document classification systems that route different document types to appropriate processing pipelines
• Implement OCR + LLM workflows that handle handwritten or poorly scanned documents

AI‑Powered Business Process Automation

• Develop AI agents that conduct multi‑step research and qualification processes autonomously
• Build conversational systems that handle routine inquiries and elevate complex cases appropriately
• Create intelligent routing and matching systems that optimize business outcomes using historical data and ML models
• Design automated scheduling and coordination systems that handle complex business logic and constraints

System Integration & Data Orchestration

• Connect internal platforms with external third‑party services, handling authentication, rate limiting, and error recovery
• Build real‑time data synchronization pipelines that keep multiple systems consistent
• Create webhook listeners and event processors that trigger automated workflows based on external events
• Design data enrichment pipelines that pull information from multiple sources and merge it intelligently

Operational Intelligence & Monitoring

• Build automated alerting and notification systems that detect anomalies and trigger appropriate responses
• Create dashboards and reporting workflows that aggregate data from multiple sources
• Develop predictive systems that use historical patterns to forecast operational needs
• Design automated quality control systems that continuously validate data accuracy across systems

AI Innovation & Experimentation

• Prototype new AI capabilities using the latest LLM models and techniques
• Experiment with vector databases, semantic search, and retrieval‑augmented generation (RAG)
• Build proof‑of‑concepts that demonstrate the business value of emerging AI technologies
• Contribute to our Technical Innovation Program (TIP) with novel automation approaches

Platform & Infrastructure

• Develop reusable automation components, templates, and design patterns
• Create internal tooling that makes it easier for other teams to build and deploy automations
• Establish monitoring, logging, and observability standards for automation workflows
• Build testing frameworks that ensure automation reliability and catch regressions