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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer - **Company:** Phoenix Contact - **Location:** Madrid, Spain (Remote available) - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Application Frameworks, User Authentication, Business Systems, Cloud Engineering, Information Engineering, Data Governance, Data Infrastructure, Data Security, Data Systems, Programming Tools, Python (Programming Language), Knowledge Management, Knowledge-Based Systems, Metadata, Search Technologies, Software Deployment, SQL Databases, Systems Integration, AI Infrastructure, Enterprise Data Management, Large Language Models, Snowflake, Generative AI, Build Management, AI Platforms, Low Latency, Data Pipelines, Automation Anywhere, Databricks - **Published:** August 21, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + 5+ years of professional experience in data engineering, platform engineering, backend engineering, or a closely related discipline, with experience building and operating production systems. + Strong proficiency in Python and SQL. + Hands-on experience building and deploying production applications or services using LLMs and generative AI. + Practical experience with RAG, embeddings, vector search, tool/function calling, AI agents, or enterprise knowledge systems. + Strong data engineering fundamentals, including data pipelines, data modeling, data quality, and secure data access. + Experience with Snowflake, Databricks, or comparable modern data platforms, together with tools such as dbt, Airflow, or similar technologies. + Proven experience building shared AI infrastructure, platforms, or reusable AI capabilities rather than focusing exclusively on individual AI applications. + Experience taking AI systems from experimentation and proof of concept through reliable production deployment. + Solid understanding of security, authentication and authorization, privacy, access control, and data governance. + Strong ability to translate ambiguous AI opportunities into practical, scalable engineering solutions. + Excellent communication and collaboration skills, with the ability to work effectively across Engineering, Product, IT, and other stakeholders. + Experience with AWS Bedrock or other managed foundation-model platforms is an advantage. + Hands-on experience with MCP (Model Context Protocol) or comparable approaches for connecting AI systems to enterprise tools and data is a plus. + Experience with LLM evaluation, AI observability, monitoring, quality management, or responsible AI practices is desirable. + Familiarity with LangChain, LangGraph, vector databases, search technologies, internal developer platforms, SDKs, APIs, or reusable infrastructure is a strong advantage. + Fluent professional proficiency in English, both written and spoken. + Reliable home internet connection suitable for fully remote work. ## Description This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Platform Engineer based in Spain. This is a senior engineering opportunity focused on building the foundational platform layer behind secure, production-grade AI applications. You will design shared infrastructure, services, integrations, and developer tooling that enable teams to build and scale AI capabilities efficiently. The role combines hands-on work with LLMs, AI agents, RAG, tool calling, and AI workflows with strong data engineering and platform engineering practices. You will leverage modern cloud and data technologies to turn experimental AI use cases into reliable, scalable production systems. Working across Engineering, Product, and IT, you will establish technical patterns, reusable capabilities, and governance standards for AI development. This fully remote role is ideal for an experienced engineer who enjoys solving foundational problems and shaping how AI is built and operated across an organization. Accountabilities: + Design and build reusable platform capabilities supporting LLM applications, AI agents, RAG, tool calling, and AI workflows. + Develop scalable data and knowledge pipelines covering ingestion, embeddings, retrieval, vector search, metadata, and knowledge management. + Build secure integrations between AI applications, enterprise data, and business systems using APIs, MCP, tool calling, and comparable integration patterns. + Develop reusable frameworks, libraries, services, SDKs, and developer tooling that allow engineering teams to build AI applications efficiently. + Establish technical standards and patterns for AI deployment, observability, evaluation, monitoring, and lifecycle management. + Define and improve approaches for measuring AI quality, accuracy, latency, reliability, security, and cost. + Take AI capabilities from experimentation and prototyping through reliable, scalable, and maintainable production deployment. + Ensure AI platform capabilities comply with security, privacy, authentication, authorization, access control, and data governance requirements. + Work with Engineering, Product, and IT teams to identify AI opportunities and translate ambiguous business needs into practical technical solutions. + Evaluate emerging AI models, frameworks, infrastructure technologies, and development approaches to determine their potential business value. + Contribute to the evolution of platform architecture, engineering standards, and reusable AI capabilities across the organization. + Promote strong data engineering and platform practices around data modeling, data quality, secure data access, and operational reliability. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI-Augmented DevOps with Platform Engineering](https://www.wearedevelopers.com/videos/1614-ai-augmented-devops-with-platform-engineering) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)