AI Engineer

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 teams 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 teams data infrastructure.

AI Architecture & Standards

• Establish the teams 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 teams 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 teams 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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