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PDG-IT

AI Engineer – AI Automation

PDG-IT

. Develop and implement automation solutions based on artificial intelligence and large language models (LLMs) .

Posted 10/28/2026full-timeSão Paulo • BrazilJuniorMid-LevelWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in developing automation solutions utilizing Artificial Intelligence and Large Language Models, with a strong focus on Python programming, API integrations, and workflow orchestration using frameworks like LangChain and LangGraph.

Highest-signal resume keywords
Python ProgrammingLarge Language Models (LLMs)LangChain FrameworkLangGraph FrameworkREST API Integrations

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Automation Solutions DevelopmentAI Agent ArchitectureData ManipulationPrompt EngineeringRAG EcosystemsMicroservicesContainerizationPerformance OptimizationTechnical DocumentationWorkflow Orchestration
Tools & Technologies
DockerAPIsAI ModelsMonitoring ToolsIntegration Tools
Industry Keywords
Artificial IntelligenceAutomationSoftware DevelopmentData Vector SolutionsPerformance Metrics

Tech Stack

Tools & technologies
DockerPython

About the role

Key responsibilities & impact
  • Develop and implement automation solutions based on artificial intelligence and large language models (LLMs)
  • Create and orchestrate complex AI agent workflows using frameworks such as LangChain or LangGraph
  • Develop robust Python scripts, APIs, and integrations to connect AI models with systems and databases
  • Optimize automation routines, ensuring the efficiency, quality, monitoring, and performance of responses generated by agents
  • Document the technical architecture, workflows, and best practices for the engineering team

Requirements

What you’ll need
  • At least 2 years of experience (mid-level) in software development and artificial intelligence projects
  • Advanced proficiency in Python applied to integrations, automation, and data manipulation
  • Practical experience developing applications with LLMs (Large Language Models) and AI agent architecture
  • Practical proficiency with frameworks such as LangChain and/or LangGraph
  • Hands-on experience creating and deploying RAG (Retrieval-Augmented Generation) ecosystems and data vector solutions
  • Knowledge of REST API integrations, microservices, and containerization (Docker)
  • Experience with prompt optimization (Prompt Engineering) and tracking/evaluating AI model metrics