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NTT DATA Logo NTT DATA · IT - Software Development

Software Development Advisor - Technical Support Engineer (Azure AI Foundry)

Mexico, Ciudad de Mexico · Job Posted June 28, 2026
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Job Description

We are currently seeking a Software Development Advisor to join our team in Ciudad de Mexico, Ciudad de México (MX-CMX), Mexico (MX). We are seeking a highly motivated Technical Support Engineer – Azure AI Foundry to support and operate AI-driven solutions built on Microsoft Azure. The ideal candidate will provide advanced technical support, assist customers in implementing and maintaining AI architectures, and troubleshoot complex issues across Azure AI services. This role focuses on operational support, implementation, and continuous improvement, rather than designing solutions from scratch. The candidate must demonstrate strong resilience, ownership, and communication skills in dynamic support environments.

Job Responsibility

  • Provide technical support for Azure AI Foundry environments and solutions
  • Troubleshoot and resolve issues related to Azure AI Foundry services (Cognitive Services, Azure OpenAI, etc.), Azure Machine Learning environments and pipelines, RAG architectures and AI agents, Azure AI Search and Azure Document Intelligence, Data pipelines, integrations, and model inference workflows
  • Support deployment, configuration, and maintenance of AI solutions in Azure
  • Analyze logs, metrics, and telemetry to identify root causes and recommend solutions
  • Collaborate with Engineering, Product, and Cloud Operations teams on escalations
  • Assist customers with implementation of reference architectures and best practices
  • Manage support incidents following SLA commitments
  • Document troubleshooting steps, runbooks, and knowledge base articles
  • Contribute to continuous improvement by identifying recurring issues
  • Support CI/CD pipelines and automation of AI workloads
  • Communicate clearly with stakeholders in English (written and verbal)
  • Maintain a proactive and resilient attitude in high-pressure environments

Requirements

  • 3+ years of experience with Microsoft Azure
  • 2+ years of experience with Azure AI Foundry (mandatory)
  • 2+ years of hands-on experience in Azure Machine Learning (mandatory)
  • 1+ year of practical experience building RAG-based agents and using MCP (Model Context Protocol or similar patterns)
  • Experience in technical support, cloud operations, or AI/ML support roles
  • Strong proficiency in Python (mandatory)
  • Strong proficiency in SQL (mandatory)
  • Experience working with Azure Machine Learning
  • Azure AI Foundry core services: Cognitive Services, Azure OpenAI, Azure AI Search, Azure Document Intelligence
  • Databases and data platforms: Azure Synapse Analytics, SQL-based systems
  • MLOps practices (model deployment, monitoring, versioning)
  • CI/CD pipelines (Azure DevOps, GitHub Actions, or similar)
  • Understanding of AI/ML lifecycle and model deployment
  • Understanding of Retrieval-Augmented Generation (RAG) architectures
  • Understanding of distributed cloud-based systems
  • Experience supporting production environments and troubleshooting complex systems
  • Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience)
  • Strong problem-solving and analytical thinking
  • Excellent communication skills (English B2 minimum
  • C1+ preferred)
  • High resilience and ability to work under pressure
  • Customer-focused mindset
  • Strong organizational and time-management skills
  • Ability to manage multiple incidents simultaneously
  • Proactive attitude and continuous learning mindset

Nice to have

  • Experience supporting AI/ML workloads in production
  • Experience with enterprise Microsoft ecosystems
  • Exposure to automation workflows and low-code platforms
  • Experience in global or 24x7 support teams
  • Experience with Copilot Studio
  • Experience with Power Automate
  • Experience with DevOps practices and infrastructure automation
  • Familiarity with data engineering workflows
  • Familiarity with Azure monitoring tools (Azure Monitor, Log Analytics)
  • Exposure to enterprise-scale AI implementations
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