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Responsibilities / Tasks

As AI Engineer, you will be responsible for designing, developing, and operating the AI agent infrastructure and tooling ecosystem at GEA. Your mandate spans the full AI development lifecycle: from defining architectural patterns and selecting the right frameworks and models, to building autonomous agents, deploying LLM-integrated tools, and continuously improving the AI systems in production.

AI Agent Development

- Design and build AI agents and multi-agent systems capable of autonomously planning and executing complex, multi-step workflows.
- Implement agentic architectures using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent.
- Define agent communication patterns, memory management strategies, and tool-use interfaces.
- Build and maintain a library of reusable agent components and interaction patterns for the team.

LLM Integration & Tool Development

- Integrate LLM APIs — including Anthropic Claude and other leading models — into internal tools and workflows.
- Develop AI-powered applications across the team's tool portfolio, including automation tools, decision support systems, and analytics assistants.
- Design and implement prompt engineering strategies, including system prompts, few-shot examples, and chain-of-thought patterns.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines that leverage the team's data infrastructure.

AI Architecture & Standards

- Establish the team's AI development standards, architectural patterns, and evaluation frameworks.
- Define best practices for model selection, prompt management, agent orchestration, and AI system observability.
- Evaluate and benchmark new models, APIs, and tooling as the AI landscape evolves.
- Contribute to technical roadmap planning and tool prioritization alongside the team lead.

Data & Infrastructure Collaboration

- Collaborate closely with the Data Engineer to design AI-ready data pipelines, feature stores, and retrieval layers.
- Ensure AI systems are grounded in reliable,



high-quality data from the team's warehouse and lake infrastructure.
- Contribute to the design of shared infrastructure — vector databases, embedding pipelines, model serving layers.

Stakeholder Collaboration & Delivery

- Work directly with business stakeholders to identify use cases, define requirements, and deliver AI-powered solutions.
- Translate complex AI concepts into accessible explanations for non-technical audiences.
- Manage end-to-end delivery of AI tool initiatives within the team's agile development process.
- Document architectures, agent designs, and integration patterns to ensure maintainability and knowledge transfer.

Your Profile / Qualifications

- Minimum 5 years of professional software or AI engineering experience.
- Proven, production-level experience building AI agents and agentic systems — not just experimentation or prototyping.
- Deep hands-on familiarity with LLM APIs (OpenAI, Anthropic, or equivalent) and agent orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar).
- Strong Python programming skills, including software design and engineering best practices.
- Solid understanding of prompt engineering techniques, RAG architectures, and LLM tool-use patterns.
- Experience deploying and operating AI systems in production environments.
- Ability to work autonomously, define technical direction, and take full ownership of complex deliverables.
- Professional-level proficiency in Spanish; working English is a strong advantage.

Nice to have:

- Experience with vector databases and embedding pipelines (Pinecone, Weaviate, pgvector, Chroma, or equivalent).
- Familiarity with cloud AI services (Azure AI, AWS Bedrock, Google Vertex AI, or equivalent).
- Experience with fine-tuning or adapting foundation models for specific domains or tasks.
- Knowledge of MLOps practices — model versioning, deployment pipelines, monitoring, and drift detection.
- Exposure to industrial, engineering, or manufacturing environments.
- Contributions to open-source AI projects or a strong portfolio of AI engineering work.
- Familiarity with data engineering concepts and platforms (SQL, Databricks) as a collaborator.

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📌 AI Engineer (Bogotá)
🏢 Gea
📍 Bogotá