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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer (MLOps & AI Integration) - **Company:** Airbus - **Location:** Albacete, Spain - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Integration Architecture, Application Performance Management, Business Software, Software Quality, Databases, Continuous Integration, Elasticsearch, Graph Database, Monitoring of Systems, Python (Programming Language), PostgreSQL, Machine Learning, Neo4j, Object-Oriented Software Development, OpenShift, Cloud Services, Software Engineering, Systems Integration, Chatbots, Large Language Models, Grafana, IT Architecture, Git, Fastapi, Pandas, Gitlab-ci, Scikit Learn, Kubernetes, Information Technology, Integration Frameworks, Search Engines, Machine Learning Operations, Kibana, Streamlit Framework, Data Pipelines, Mixed Reality, Docker - **Published:** September 15, 2026 - **Apply:** https://ag.wd3.myworkdayjobs.com/Airbus/job/Albacete/Data-Scientist---Artificial-Intelligence-m-f_JR10389233-1 ## About the Role * Experience: 3 to 4+ years of full-time, post-graduate industry experience in Machine Learning Engineering, MLOps, or Software Engineering. Please note: while we highly value internships, apprenticeships, and academic projects, this specific role requires an established track record in a corporate/enterprise environment. We would be delighted to read, at the top of your resume or cover letter, a brief example of a ML model or Gen AI solution you have put into production. * Education: Master's degree or equivalent in Computer Science, Software Engineering, or Artificial Intelligence. * Mindset: Highly autonomous but a strong team player. You thrive in bridging the gap between Data Science (algorithmic focus) and IT (production and infrastructure focus). * Languages: Fluency in English and Spanish is mandatory to collaborate effectively across our international hubs and local IT teams. (French or German is a plus). * Mobility: Open to occasional travel to Marignane and Donauwörth to align on global AI strategies., This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company's success, reputation and sustainable growth. ## Description You will join the Digital Innovation Team within Airbus Helicopters' Transformation Department. Our mission is to bridge the gap between applied research and concrete business value, leveraging technologies like Artificial Intelligence, Advanced Analytics, and Mixed Reality. While you will be the core representative of our Digital Innovation team in Albacete, you will work in a deeply integrated, international environment with our hubs in Marignane (France) and Donauwörth (Germany). Being physically located alongside the Albacete IT teams, you will act as a strategic bridge to ensure our AI innovations are robustly integrated and scaled into enterprise systems., We are looking for a Machine Learning Engineer to help bridge the gap between our Data Science Proof of Value (PoVs) and enterprise-scale production. This is not an exploratory Data Science or pure modeling role. We are looking for a robust hands-on software and ML engineer who understands IT infrastructure, CI/CD, and how to scale AI solutions securely. You will leverage your technical expertise to collaborate with local IT teams, helping to design practical, scalable solutions for deploying AI projects (e.g., ML models, GenAI/chatbots). Alongside this cross-functional integration work, you will also provide hands-on platform support and tooling for our internal Data Scientists., * Industrialization & CI/CD: Collaborate with IT teams to define and implement best practices for transitioning ML models from PoV to production on OpenShift (On-Premise) and GCP. Build, maintain, and improve CI/CD pipelines to ensure code quality and secure deployments. * GenAI & AI Integration: Implement the technical integration of AI solutions (such as chatbots, LLMs, or predictive APIs) into existing business applications. This includes managing complex data ingestion pipelines for RAG architectures, using search engines, and working with vector and graph databases. * Monitoring & Resource Optimization: Set up dashboards to monitor model health, API performance, and detect data drift. Optimize compute resources (CPU/GPU) to ensure cost-efficiency across our infrastructure. * Platform Support & "Starter Kits": Streamline the work of our Data Science team by creating standardized, ready-to-use templates (Docker/Git) for new PoVs, maintaining optimized environments, and resolving complex dependency issues. * Cross-functional Collaboration: Act as a proactive technical liaison between the international Innovation team (prototyping) and the Albacete IT department (infrastructure). You will translate Data Science compute needs into IT architecture specifications and ensure our projects comply with strict cybersecurity standards. Tech Stack You Will Use * Infrastructure: OpenShift AI (On-Premise) as a primary environment, with GCP for cloud deployments. * Containers & Orchestration: Docker, Kubernetes. * Databases & Search: PostgreSQL (including pgvector), Elasticsearch, and Graph Databases (e.g., Neo4j). * Programming: Python (Advanced) and standard software engineering practices (OOP, APIs with FastAPI). * MLOps & CI/CD: MLflow, Git, GitLab CI/CD (or similar). * AI/Data Science Tools: Pandas, Scikit-Learn, Streamlit, GenAI integration frameworks (LangChain, etc.), and monitoring tools (Grafana/Kibana). ## Related Videos - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)