GEA is an international engineering company and one of the largest suppliers for the food, beverage, chemical, pharmaceutical and farm technology industries. In fact, without realizing it, you have likely eaten, drunk or taken medication produced with GEA equipment in the last 48 hours. We are focused on supporting our clients as we all work towards more sustainable production in all the industries we support.
The world today needs better. For our planet and future generations.
To support our growing business, we are looking for an AI Engineer in Bogota, Colombia.
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.
Did we spark your interest?
Then please click apply above to access our guided application process.
