Machine Learning Engineer

Job Description
An opportunity for an Machine Learning Engineer has arisen within Airbus Helicopters (Albacete, Spain).
WHY JOIN US?
LIFE IN ALBACETE: QUALITY & CONVENIENCE -
Albacete is a dynamic, historic city in Spain, located in the region of Castilla-La Mancha. This modern urban center, known for its rich cultural heritage, and it is conveniently close to Madrid and the Mediterranean coast.
Forget long commutes. Albacete offers you the great advantage of a "15-minute city": safe, accessible, comfortable and perfectly connected. Enjoy a premium standard of living at a competitive cost, ensuring you have the perfect balance between your career and your personal life.
AIRBUS HELICOPTERS: GROW WITH US
We offer more than a job; we offer a community. Immerse yourself in a young, collaborative environment that feels like family. As a strategic Center of Excellence, we are a global leader in helicopter production, offering you endless potential to grow alongside our major projects.
THE PROJECT: SHAPING THE FUTURE
Welcome to the Digital Campus , a pioneering ecosystem dedicated to industrial digitalization and innovation. This is more than just a workplace; it is a technological hub where we define the future of flight.
Bring your ideas to a dynamic, modern ecosystem and help us build the next generation of aerospace solutions.
About The Team & The Environment
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.
The Role
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.
Key Responsibilities

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 (em. 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).

Who You Are

Experience:

3 to 4+ years of full‐time, post‐graduate industry experience in Machine Learning Engineering, MLOps, or Software Engineering. Plea