DaCodes

Nov 13, 2026

Applied AI Engineer - LLMs & Agentic Systems | Remote Mexico/LATAM

RemoteMexico

Mexico

Python

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About the opportunity

We are looking for an Applied AI Engineer to join the technology team of a fintech company that develops solutions for international transfers and remittances, operating primarily between the United States, Mexico, and Central America.

The position is focused on designing, developing, and bringing to production artificial intelligence solutions based on Large Language Models (LLMs), agentic systems, and information retrieval (RAG).

We are looking for someone who has worked directly on building these solutions, who can make informed technical decisions, and who understands how to ensure their quality, reliability, and performance in real-world environments.

This is not a position focused exclusively on traditional Machine Learning or model experimentation. The primary focus is the engineering of AI solutions applied to business problems.

What we look for in a candidate

Technical ownership: ability to explain what you built, what decisions you made, and what results you obtained.

Architectural judgment: choosing solutions based on their utility and constraints, not the popularity of a technology.

Production orientation: considering quality, errors, monitoring, costs, and maintenance from the design phase.

Critical thinking: questioning and clarifying requirements before proposing a solution.

Communication: explaining technical decisions and their implications to both technical and business stakeholders.

Autonomy: solving problems with initiative and taking responsibility for the results.

What kind of experience are we particularly interested in?

We are interested in learning about projects where you participated directly in the development of a solution with LLMs or agents that was used by real users.

During the process, we will delve into the business problem, the implemented architecture, the decisions you personally made, the evaluation mechanisms, and the challenges that arose during operation.

We value depth and demonstrable responsibility in real projects more than the number of tools or frameworks mentioned in the CV.

Responsibilities

Design, develop, and maintain LLMs-based applications used in real business processes.

Build agentic systems capable of interacting with tools, APIs, and external services, incorporating controls over their actions.

Evaluate when it is appropriate to use agents, deterministic workflows, or a combination of both.

Design and implement RAG solutions, including strategies for chunking, embeddings, retrieval, reranking, and context management.

Implement evaluation mechanisms to measure response quality, information retrieval, and agent behavior.

Identify and resolve issues related to hallucinations, execution errors, latency, costs, and reliability.

Define validation, error handling, and monitoring mechanisms for solutions deployed in production.

Collaborate with product and engineering teams to understand business requirements, evaluate alternatives, and convert them into viable technical solutions.

Participate directly in architecture, implementation, and continuous improvement decisions for the developed solutions.

Essential Requirements

Experience with LLMs and agentic systems

Practical experience building and operating LLMs-based applications in production, beyond prototypes or proofs of concept.

Direct participation in architecture and implementation decisions for agent-based solutions.

Experience implementing tool calling and integrations with APIs or external services.

Practical knowledge of context, state, and memory management in LLMs applications.

Ability to justify when to use an agent and when to solve a problem using deterministic logic.

RAG and information retrieval

Experience implementing RAG pipelines for real-world applications.

Knowledge of chunking strategies, embeddings, semantic search, and document retrieval.

Experience evaluating and improving the relevance of retrieved information.

Ability to identify retrieval issues and reduce incorrect or unsupported responses.

Evaluation and operation

Experience implementing quality evaluations for LLMs applications, including testing responses and agent behavior.

Practical knowledge of mechanisms to detect and mitigate hallucinations, errors, and inconsistent responses.

Experience identifying latency issues, token consumption, and inference costs.

Ability to diagnose failures and improve the reliability of solutions in production.

Software engineering

Solid experience developing solutions with Python.

Experience integrating APIs, backend services, and language models.

Best practices for development, testing, error handling, and application maintenance.

Autonomy to investigate problems, propose alternatives, and execute solutions from start to finish.

Desirable Knowledge

Experience with agent orchestration frameworks such as LangGraph, Pydantic AI, or similar.

Experience implementing integrations using Model Context Protocol (MCP).

Knowledge of vector databases and semantic search tools.

Experience with observability and evaluation tools for LLM applications.

Knowledge of Machine Learning and Data Science.

Experience in fintech, financial services, payments, fraud, or regulated processes.

Familiarity with security controls and protection of sensitive information in AI applications.

🚀 Integration with global brands and disruptive startups.

🏡 Remote work/Home office.

⏳ Schedule adjusted to the assigned work cell/project.

📅 Monday to Friday work week.

🎉 Day off on your birthday.

🏥 Major medical insurance (applies to Mexico).

🛡️ Life insurance (applies to Mexico).

🎓 Access to courses and certifications.

📢 English classes.

🏆 Opportunities within our different business lines.

Languages

Applied AI Engineer - LLMs & Agentic Systems | Remote Mexico/LATAM

About the opportunity

What we look for in a candidate

What kind of experience are we particularly interested in?

Responsibilities

Essential Requirements

Experience with LLMs and agentic systems

RAG and information retrieval

Evaluation and operation

Software engineering

Desirable Knowledge

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