AI Engineer

Worldwide | Sept. 29, 2026

Report as Closed

Company: Grupo Mariposa

Country: Worldwide

Type: Remote

Employment: Full-time

Description: We are a multinational food and beverage corporation founded in 1885 with operations in more than 14 countries, with more than 15,000 employees.

About the company

Grupo Mariposa is a multinational food and beverage corporation, founded in 1885, with operations in more than 14 countries and more than 15,000 employees. We have the largest beverage portfolio in the region and strategic alliances with PepsiCo and AB InBev. We are organized into 4 business units: apex (transformation), cbc (distribution), beliv (innovation in beverages) and bia (food). We are looking for talents to join our expansion and growth strategy, sharing our desires and contributing their vision to achieve great results.

We are looking for the "Data Engineering Lead" to lead the corporate data platform in a multi-cloud environment, with a focus on Azure and support on Google Cloud Platform (GCP). This role defines, leads and scales the data engineering strategy for the region, ensuring a robust, governed and scalable platform for advanced analytics, BI and digital products in multiple countries.

• • 4+ years in software engineering, with 1-2+ years building generative AI solutions/agents in production (not just POCs).
  • Practical experience with an enterprise agent platform: Microsoft Azure AI Foundry, AWS Bedrock Agents, Google Vertex AI Agent Builder, or frameworks such as LangGraph / AutoGen.
  • Solid knowledge of RAG: vector foundations, embeddings, chunking, source integration of enterprise data type Databricks/Unity Catalog.
  • Experience with MCP (Model Context Protocol) or tool-calling / function calling architectures in LLMs.
  • Experience designing guardrails, input/output validation and security patterns for AI systems (content safety, prompt injection, PII management).
  • Management of event-oriented architectures (Kafka or similar) and data patterns. idempotence.
  • Experience with REST APIs, API management (Apigee, Kong or similar) and zero-trust principles.
  • Cloud: solid experience in Azure (preferred) or equivalent AWS/GCP.
  • Spanish and English (the team operates in Spanish; technical documentation frequently in English).

About the company

We are a multinational food and beverage corporation with regional operations, a broad portfolio of brands and an accelerated digital transformation strategy. Within Apex Digital / M5, the Data & Analytics area enables analytical products, governed data and advanced capabilities for business units, including CBC, Beliv, BIA and cross-cutting digital transformation initiatives.

As part of this evolution, the organization is moving towards an enterprise AI Agents architecture based on Databricks, ADLS Gen2, Unity Catalog, Azure AI / Microsoft Foundry, Copilot Studio and Power Automate, seeking to enable secure enterprise assistants and agents, traceable, scalable and connected to core business data.

Design, build, deploy and evolve generative AI solutions and enterprise agents on Azure AI / Microsoft Foundry, integrated with the corporate data platform, to enable business use cases with traceability, security, scalability and high operational and commercial impact.

Key responsibilities of the position

• Design generative AI solutions, assistants and enterprise agents using Azure AI, Microsoft Foundry and related services.
• Define architecture reference for RAG, agents, tools, evaluation, security and observability.
• Build APIs, microservices and backend components in Python to integrate models, tools and corporate systems.
• Connect AI solutions with Databricks, Unity Catalog, ADLS Gen2, Azure AI Search, Power Automate, Copilot Studio, Teams, web channels and other internal systems.
• Establish grounding, access control, versioning, testing, evaluation and monitoring responses.
• Collaborate with Data Engineering, BI, Architecture, Security, Product and business areas to bring use cases to production.

Alignment with the Agents AI Architecture:

• Develop the technical blueprint of the proposed architecture, defining components, patterns, flows, environments and deployment criteria.
• Implement the enterprise knowledge base pattern by connecting Databricks, ADLS Gen2 and Foundry IQ / search and grounding capabilities.
• Configure and develop agents or copilots on Copilot Studio / Foundry, with invocation of actions through Power Automate or backend services.
• Define observability, evaluation, security and governance standards for the operation of AI solutions.

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