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Job Description
http://coches.net, http://motos.net, and Milanuncios are seeking an AI & Automation Engineer to help our teams seamlessly incorporate artificial intelligence and streamline workflows. In this pivotal role, you will collaborate across the organization to drive innovation by designing automations and integrating generative AI technologies. Your work will ensure that AI initiatives create meaningful operational impact, empowering our teams to work smarter and shaping the future of our internal tools and processes.
Responsibilities
- Identify opportunities across different departments to implement AI solutions and automate repetitive workflows.
- Integrate Generative AI capabilities into internal processes and products, leveraging Amazon Bedrock to deploy and manage foundation models.
- Develop and connect custom tools and data sources to our AI agents using the Model Context Protocol (MCP).
- Collaborate hands-on with Product, Engineering, Data, and non-technical departments to enable them to adopt AI solutions effectively.
- Champion ethical and responsible AI development, ensuring data privacy and security in all automated workflows.
- Stay at the forefront of AI advancements, continuously researching new ways to combine LLMs with automation platforms.
- Translate complex AI/automation capabilities into clear business value and train internal teams on how to leverage these new tools.
- Develop, train, and deploy traditional Machine Learning models (e.g., predictive analytics, classification) to solve specific business problems.
- Process and transform large-scale datasets to fuel AI models and automations, utilizing Apache Spark and Databricks.
- Implement and manage LLM observability, prompt engineering workflows, and evaluation metrics using Langfuse.
- Migrate, design, build, and maintain complex, reliable automated workflows using n8n.
Qualifications
- Proven experience (5+ years) in software engineering, automation, or AI-focused roles.
- Advanced Spanish and intermediate English communication skills.
- Track record of successfully delivering and maintaining automation workflows (APIs, webhooks, data pipelines) into production.
- Deep understanding of GenAI, LLMs, AI Agents, and RAG (Retrieval-Augmented Generation) architectures.
- Hands-on experience with workflow automation platforms, specifically n8n.
- Knowledge of Python and/or Node.js/TypeScript for scripting and API integrations.
- Practical experience implementing observability and analytics for LLMs using Langfuse (or similar tools).
- Familiarity with the Model Context Protocol (MCP) for extending AI agent capabilities.
- Strong strategic thinking, problem-solving, and communication skills to bridge the gap between technical and non-technical teams.
- Advanced experience using AI coding tools such as Anthropic Claude Code, GitHub Copilot, and other AI-powered development assistants to accelerate software engineering, automation, debugging, and integration workflows.
Preferred Qualifications & Skills
- Experience conducting internal workshops or evangelizing new technologies within a company.
- Experience with broader AWS cloud infrastructure and deployment.
- Familiarity with AI ethics, data privacy, and internal governance.
- BS or MS in Computer Science, Software Engineering, or a related field (or equivalent practical experience).
Key Responsibilities
Identify opportunities to implement AI solutions and automate repetitive workflows across departments.
Integrate Generative AI capabilities into internal processes using Amazon Bedrock.
Develop and connect custom tools and data sources to AI agents using the Model Context Protocol.
Collaborate with Product, Engineering, and Data teams to enable AI adoption.
Champion ethical AI development and ensure data privacy and security.
Research and combine LLMs with automation platforms.
Translate AI capabilities into business value and train internal teams.
Develop and deploy traditional Machine Learning models for specific business problems.
Process and transform large-scale datasets using Apache Spark and Databricks.
Implement LLM observability and prompt engineering workflows using Langfuse.
Migrate and maintain automated workflows using n8n.