AI Engineer
Metova
- Location
- Onsite (United States)
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
Metova is seeking an AI Engineer to design and supervise the technical architecture of AI solutions using intelligent agents and LLMs. This role involves implementing multi-agent coordination and autonomous flows to map business needs.
Skills
Artificial Intelligence
Autonomous Agents
RAG Architectures
Vector Stores
LangChain
LlamaIndex
Python
FastAPI
MLOps
Docker
Kubernetes
MCP Protocol
A2A Protocol
Asynchronous Architectures
Pydantic
Asyncio
Perks
- Remote OK
Full job details
A leading company in Mexico specializing in accounting software is looking for a highly skilled AI Engineer to join the team.
REQUIREMENTS:
- 5 years of experience in artificial intelligence projects and 2 years in the implementation of autonomous agents or co-pilots.
- Fluent technical English.
- Experience working with business data in domains such as accounting, finance, payroll, billing, or ERP.
- Experience working with vector stores (Chroma, Weaviate, Pinecone) and RAG architectures.
KNOWLEDGE AND SKILLS:
- Handling frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar.
- Practical knowledge of MCP and A2A protocols, use of tools, memory management, and conversation status.
- Solid command of Python and experience with FastAPI, asyncio, Pydantic, and asynchronous architectures.
- Knowledge of MLOps: CI/CD, Docker, Kubernetes, agent monitoring, and automated retraining.
- Practical knowledge of other languages such as Golang, Java, or C# (.NET), especially in building high-performance components (Nice to Have).
RESPONSABILITIES:
- Define, design, and supervise the technical architecture of solutions based on intelligent agents and LLMs, integrating tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent frameworks.
- Implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) architectures to enable multi-agent coordination and autonomous flows within business environments.
- Work with the MLOps team and execution environments that enable continuous agent updating and deployment, including memory management, context, and long-term planning.
- Collaborate closely with product, UX, data, and backend teams to map business needs to intelligent agent architectures.