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CRH Talento de IT

Lead AI Engineer & Architect

United States

Ciudad de México, Mexico · Colorado Springs, Colorado, United States · Brazil

PythonJavaSQLAWSGCP
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💼 Salary Range

Modality: Remote

Location: Mexico, Colombia, or Brazil

English: B2/C1

  1. Mexico: $90,000 – $140,000 MXN monthly
  1. Colombia: $17,000,000 – $22,950,000 COP monthly
  1. Brazil: R$18,000 – R$29,000 BRL monthly

About the position

We are looking for a Lead AI Engineer & Architect to lead the design and development of an innovative AI-powered FinOps platform.

The position will play a key role as Technical Lead and Multi-Agent Systems Architect, participating in the construction of a greenfield platform composed of multiple specialized AI agents capable of analyzing information, generating insights, detecting anomalies, optimizing resources, and executing actions autonomously.

You will work in a multi-cloud environment (GCP/AWS), defining the architecture, orchestration, security, governance, and scalability of the agents, as well as the necessary mechanisms to control costs and ensure compliance with business and financial standards.

🤖 What kind of solutions will you develop?

The platform's AI agents will be able to:

  1. Analyze large volumes of information and generate insights.
  1. Detect anomalies.
  1. Identify resource optimization opportunities.
  1. Execute automated actions.
  1. Support financial operations.
  1. Optimize cloud infrastructure costs.
  1. Interact with APIs and enterprise tools.
  1. Escalate actions to human approval processes when necessary.

🚀 Key Responsibilities

  1. Design and lead the architecture of an AI multi-agent systems platform.
  1. Oversee the implementation and orchestration of approximately 8 specialized agents.
  1. Define communication between agents, tools, and services.
  1. Lead the prompt engineering, tool calling, and structured outputs lifecycle.
  1. Design solutions using Python, FastAPI, Pydantic, and FastMCP.
  1. Implement and manage the routing of different LLMs through LiteLLM Gateway and Vertex AI Model Garden.
  1. Work with models such as Gemini and Claude.
  1. Design Tokenomics mechanisms to monitor and control the consumption and cost of LLMs.
  1. Implement guardrails and execution limits for autonomous agents.
  1. Design security controls and Zero Trust architectures.
  1. Ensure segregation of duties and human approval mechanisms for sensitive financial processes under SOX standards.
  1. Design integrations using APIs, events, and fallback systems like Jira, Slack, and Microsoft Teams.
  1. Define code standards, testing, APIs, schemas, and error handling.
  1. Perform code reviews and establish good engineering practices.
  1. Mentor Senior and Semi-Senior AI engineers.
  1. Collaborate on designing scalable architectures on GCP and AWS.

🧠 Must-Have Requirements

  1. 8+ years of experience in Software Engineering.
  1. 3+ years in technical leadership, architecture, or AI solutions leadership roles.
  1. Hands-on experience building solutions based on LLMs and/or multi-agent systems.
  1. Advanced proficiency in Python 3.11+.
  1. Experience with FastAPI and Pydantic.
  1. Experience with MCP/FastMCP.
  1. Experience with Google Cloud, specifically:
  1. Vertex AI
  1. BigQuery
  1. GKE
  1. Workload Identity
  1. Experience with Google ADK and/or agent frameworks.
  1. Experience with LiteLLM Gateway or similar LLM routing solutions.
  1. Experience with RAG.
  1. Knowledge of Graph RAG and/or Hybrid RAG.
  1. Experience in prompt engineering, tool calling, and structured outputs.
  1. Security knowledge such as Zero Trust, OAuth, RBAC, and Workload Identity.
  1. Experience with audit logging and security controls.
  1. Knowledge of error specifications for APIs/MCP, ideally RFC 7807/9457.
  1. Experience with object-oriented enterprise languages, preferably Java.
  1. English B2/C1

⭐ Bonus Points

  1. Experience in FinOps and cloud cost optimization.
  1. Experience in financial or enterprise platforms.
  1. Experience with SOX and segregation of duties.
  1. Experience controlling LLM costs and token consumption.
  1. Experience with OLAP architectures, SQL, and data schemas under standards like FOCUS.
  1. Experience implementing autonomous agents capable of executing actions on enterprise systems.

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