AI/ML Architect

Há 3 dias

Região Geográfica Intermediária de São Paulo, Brasil beBeeExpert Tempo inteiro US$4.000 - US$8.000
Job Overview

This is a unique opportunity to work as a AI/ML Architect, responsible for designing and deploying intelligent systems that utilize cutting-edge AI technologies. The role requires expertise in architecting scalable, distributed AI/ML systems, with a strong focus on LangChain orchestration, autonomous AI agents, and robust cloud infrastructure.

Key Responsibilities:
  • Design and deploy intelligent systems leveraging AI technologies.
  • Develop expertise in LangChain orchestration, autonomous AI agents, and cloud infrastructure.
  • Work across multiple functions, including internal projects, sales engineering, AI-first engineering and innovation sprints, and forward-deployed engineering.
Requirements:Technical Requirements:
  • Minimum 4 years of experience in AI/ML development with production deployments.
  • Fluent English required for direct client interaction.
  • Reliable workspace and internet availability.
  • Strong system architecture experience.
  • Expert-level LangChain experience for AI orchestration and workflow management.
  • Hugging Face experience - transformers, model integration, and deployment.
  • Extensive AI Agent development with LangChain or Google Vertex AI.
  • Heavy AWS cloud experience, particularly with Bedrock, SageMaker, and AI/ML services.
  • Backend generalist comfortable with Node.js and Python for AI service development.
  • Agile methodologies experience.
AI Agent & LangChain Expertise (Required):
  • LangChain framework mastery for complex AI workflow orchestration.
  • Hugging Face integration - transformers, model deployment, and API integration.
  • AI Agent architecture design with LangChain or Google Vertex AI.
  • Prompt engineering and chain-of-thought optimization.
  • Vector databases and embedding systems.
  • RAG pipeline development and optimization.
  • LLM integration across multiple providers.
  • Agentic system design with memory, planning, and execution capabilities.
Backend & Cloud Infrastructure:
  • Heavy AWS Cloud services experience.
  • System architecture design for high-scale, distributed AI/ML applications.
  • Microservices architecture and design patterns for AI systems at scale.
  • Node.js and Python backend development for AI service APIs.
  • RESTful API design and GraphQL for AI service integration.
  • Database design and management for AI data workflows.
  • Modern JavaScript/TypeScript and Python async programming.
MLOps & Integration:
  • CI/CD pipelines and GitHub Actions for ML model deployment.
  • Model versioning, monitoring, and automated retraining workflows.
  • Container orchestration for AI services.
  • Performance optimization for high-throughput AI systems.
  • Modern authentication and secure API design for AI endpoints.
  • API security implementation.

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