Job at a glance
Job Description: Description: At Airbus, we are harnessing the power of artificial intelligence to enhance efficiency and quality across our value chain. Our team is composed of technologists and business leaders dedicated to innovation and excellence. We are seeking a visionary, highly skilled, and innovative AI Engineer (4–7 Years) to join our high-impact team. In this role, you will architect, build, and deploy production-grade AI-driven products designed to automate complex engineering workflows, accelerate software transformation, and drive intelligent digital paradigms.
You will turn ambiguous, cutting-edge AI concepts into scalable, reliable, cost effective and high-performing enterprise platforms. Qualification & Experience: Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related quantitative field. Required Certification: Must hold at least one recognized cloud or AI certification (e.g., Google Cloud Professional Machine Learning Engineer or equivalent advanced AI/Cloud credentials).
Experience: 4 to 7 years of hands-on experience in building, deploying, and scaling end-to-end AI/ML products, generative AI applications, code transformation tools, and intelligent software automation systems. Key Responsibilities End-to-End AI Product Engineering: Lead the lifecycle of advanced AI products—from architectural design and model selection/fine-tuning to production deployment, monitoring, and performance optimization.
Intelligent Automation & Modernization Solutions: Design and implement intelligent systems that parse, translate, and modernize complex legacy codebases and technical documentation using state-of-the-art Natural Language Processing (NLP) and Large Language Models (LLMs). Prompt Engineering & Model Fine-Tuning: Develop robust prompt architectures, retrieval-augmented generation (RAG) pipelines, and fine-tuned models to automate domain-specific artifact generation and technical decision-making from high-level user prompts.
Cloud Architecture & Scalability: Leverage Google Cloud Platform (GCP) infrastructure to build resilient, serverless, and scalable AI microservices and batch processing pipelines. Cross-Functional Collaboration: Partner closely with Product Managers, UX Designers, Software Architects, and Domain Experts to ensure technical feasibility, clear system requirements, and frictionless integration into enterprise ecosystems.
Code Quality & Best Practices: Maintain high engineering standards by establishing CI/CD pipelines for AI assets, automated testing frameworks, robust API design, and comprehensive technical documentation. Advocacy & Mentorship: Drive an innovation-first culture across the engineering lab, staying ahead of emerging Generative AI/ML research and mentoring junior team members on production ML engineering best practices.
Cloud Infrastructure & AI FinOps: Architect resilient, serverless, and scalable AI microservices on Google Cloud Platform (GCP) while implementing granular tagging, billing telemetry, and cost-attribution frameworks for all AI workloads. Cost Tracking & Optimization: Monitor, analyze, and optimize model inference costs (token-based API spend, vector database queries, GPU/TPU utilization) to maintain full visibility into product operational costs.
Technical Essentials Cloud Platform Mastery: Extensive expertise in Google Cloud Platform (GCP) , including Vertex AI, Cloud Run, BigQuery, Cloud Functions, and GKE. Generative AI & LLM Frameworks: Strong proficiency in applying LLMs, RAG architectures, vector databases (e.g., Pinecone, ChromaDB, Vertex Vector Search), and frameworks like LangChain or LlamaIndex to build complex software automation tools.
Programming & Software Engineering: Mastery of Python and solid proficiency in modern web/backend stacks (RESTful APIs, gRPC, microservice design patterns, modern frontend frameworks). Code Parsing