AI Solutions Engineer (d/f/m), München

Airbus pioneers sustainable aerospace for a safe and united world. The Company constantly innovates to provide efficient and technologically-advanced solutions in aerospace, defence, and connected services. In commercial aircraft, Airbus offers modern and fuel-efficient airliners and associated services. Airbus is also a European leader in defence and security and one of the world's leading space businesses. In helicopters, Airbus provides the most efficient civil and military rotorcraft solutions and services worldwide.

Tasks

• Problem Discovery & Feasibility: Partner with business leads, technical teams to review feasibility of AI use cases, identify technical bottlenecks and lead development of the required AI solutions from GenAI prompt engineering requests, to implementing RAG (retrieval augmented generation) pipeline, and end-to-end orchestrating of a full agentic solution.
• Tactical Implementation: Evaluate implementation requirements of AI use cases across various Airbus networks / deployment environments and available technology to make critical decisions to minimize end-user / operations impacts and individual AI use cases on the business. This may involve revision of API services, data connectors with tech. teams, optimization of prompts with end-users, and fine-tuning of available LLM (large language models) for optimal fit-for-purpose.
• Implementation of Agentic Systems: Where appropriate, drive key decisions on the design / implementation of Agentic AI workflows, enabling the GenAI platform to act as an orchestrator of multi-staging tasks, internal platforms, systems and tools while ensuring adherence to ethical guidelines of AI use.
• Platform Evolution: Act as forward deployed technical liaison and subject matter expert positioned between AI development teams and business teams, actively capturing issues, incidents, failures of individually deployed AI use cases and feed to development teams for future platform evolution.
• Sovereignty & Compliance: Leverage understanding of applicable EU AI compliance policies, security standards and data sovereignty regulations for secure deployment and operations of AI use cases in Airbus environment.
• AI solutions delivery: Manage the end-to-end data science lifecycle for integrating GenAI features into existing solutions, focusing on data preparation, LLM fine-tuning, RAG implementation, and defining rigorous evaluation metrics to enhance the performance of the internal solution.

Profile

• Bachelor's Degree in computer science, software engineering, data science or other related technical discipline or preferably a Master’s Degree in Artificial Intelligence (AI), Data Science, or Computer Science with specialization in cloud computing, DevOps and/or AI is preferred, reflecting the advanced nature of the role.
• A strong understanding of standard AI / GenAI/ Agentic AI development and implementation approaches into cloud platforms (AWS/GCP) and into containerized solutions (via Docker, Kubernetes, OpenShift).
• Expertise in development and implementation of LLMs, RAG pipelines, and Agentic AI across distributed environments and networks.
• Expertise in Agile, DevOps methodologies, and CI/CD (Continuous Integration / Continuous Deployment) practices, essential for the end-to-end delivery focus.
• Experience with scripting languages (e.g. Python, JavaScript, etc.) for automation and ML/DevOps libraries.
• At least 5+ years continuous working experience in a multinational company in software engineering, data science, and forward-deployed technical roles.
• Previous experience as a liaison between tech development and business teams
• Python / C++ expert level and experience working across the full stack applications to develop / integrate AI solutions into technology platforms, systems, network environments, etc.
• Mastery of vector databases, graph databases, and experience with basic data ingestion / data quality principles and workflows for evaluation of data sets for AI consumption.
• Experience with implementing rigorous scientific evaluation frameworks for optimal AI outputs.
• Recommended additional industry-recognized certifications (e.g. cloud platform certification, DevOps / Container certification, MLOps-specific certifications, etc.)
• Excellent problem-solving and analytical skills, with the ability to work independently in a fast-paced environment.
• Strong communication and collaboration abilities, and comprehensive end-user support and stakeholder management skills and ability to work independently and as part of a team in a fast-paced environment.
• Fluent English, German and English is a plus