> Markdown version of [/jobs/ext/1308092-ai-data-engineer](https://www.wearedevelopers.com/jobs/ext/1308092-ai-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Engineer - **Company:** TechBiz Global GmbH - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, ARM Architecture, Cloud Computing, Cloud Computing Security, Computer Programming, Data Architecture, Information Engineering, Data Infrastructure, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Software Engineering, AI Infrastructure, Snowflake, Generative AI, Amazon Virtual Private Cloud (VPC), Data Lakes, Production Code, Machine Learning Operations, Streamlit Framework, Serverless Computing - **Published:** July 17, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role Expert-level Snowflake: Extensive hands-on experience with Cortex, including setup, management, and cost optimization. Snowflake Suite: Deep expertise (SME) in Snowpark and Streamlit. Programming: Advanced proficiency in Python and a strong background in Software Engineering. AI Infrastructure: Proven experience in MCP (Model Context Protocol) server development and configuration. Cloud & Data: Deep understanding of data modeling, data architecture, and AWS environments (specifically AWS Bedrock). Proficiency in core AWS infrastructure: S3 (data lakes), IAM (permissions/security), Lambda (serverless compute), and VPC/Networking (secure cloud connectivity). Seniority: Minimum 5+ years of experience in data/infrastructure engineering, showing the ability to work independently and interface directly with internal technical stakeholders. Nice-to-Have: GenAI/RAG: Practical experience deploying Generative AI and Retrieval-Augmented Generation (RAG) systems in a production setting. Machine Learning: A background in ML engineering or MLOps (e.g., experience with AWS SageMaker). Open Source: Experience contributing to or managing open-source AI tooling like Goose. ## Description We are seeking a highly senior, hands-on AI Data Infrastructure Engineer (potentially at a Lead level) to architect and own our institutional AI foundation. This is a specialized role at the intersection of Data Engineering and Software Engineering. Unlike a traditional AI Developer, your focus will be on the infrastructure, tooling, and ecosystem that powers AI, rather than building individual end-user solutions. You will modernize our data environment, making it "AI-ready," and ensure our platform is robust, scalable, and cost-optimized to support the next generation of online education and healthcare simulations. About the Client Our customer is a leader in online education, dedicated to empowering professionals through innovative simulation and learning platforms. We are a certified great workplace, ranked consistently by Fortune as a top employer for Millennials and Women. You will join the Data, AI & Automation (DAIA) team-a tight-knit, remote-first group of passionate experts driven by curiosity. We work in a fast-paced environment where we value "human-centric" AI that solves real-world problems in the healthcare and allied health fields., Platform Ownership: Set up, maintain, and own the core AI platform infrastructure with a primary focus on Snowflake Cortex and its surrounding ecosystem. Infrastructure as Code & Tooling: Configure and maintain MCP (Model Context Protocol) servers and manage the integration of open-source packages (e.g., Goose). Cost & Performance Optimization: Actively manage Snowflake credits, token usage, and overall system performance to ensure a cost-effective and resilient environment. Data Architecture: Modernize and refine high-level platform architecture, ensuring external datasets are seamlessly integrated and "AI-ready." Observability: Implement and maintain high standards for system monitoring, observability, and reliability. Technical Leadership: Act as a self-starting, independent lead who can translate high-level infrastructure needs into functional, production-grade code. ## Related Videos - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [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) - [WeAreDevelopers LIVE - CSS is DOOMed](https://www.wearedevelopers.com/videos/1838-wearedevelopers-live-css-is-doomed) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [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) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) ## Related Articles - [Got AI ideas but no money? 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