AI Engineer

Job Description:

Role : AI Engineer

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 & AST Analysis: Familiarity with abstract syntax trees (ASTs), static code analysis, compiler concepts, or domain-specific language (DSL) translation techniques.
• MLOps & DevOps: Hands-on experience with MLOps workflows, model tracking, automated testing, containerization (Docker, Kubernetes), and CI/CD pipelines.
• AI Cost Monitoring & FinOps: Proven experience in token metering, cost attribution, model routing policies (balancing frontier models vs. smaller open-source models for cost efficiency), and monitoring tools (OpenTelemetry, Cloud Monitoring). 

Soft Skills & Behavioral Attributes

• Strategic Product Mindset: Ability to translate complex client or internal business requirements into practical, scalable AI features with measurable ROI.
• Articulate Communication: Exceptional verbal and written communication skills with a proven ability to explain complex AI/ML mechanics to non-technical business leaders and senior technical stakeholders alike.
• Innovative & Creative Problem-Solver: Thrives in an ambiguous, lab-oriented environment where novel user experience and software engineering paradigms must be invented from scratch.
• High Empathy & User Advocacy: Deep passion for understanding engineering pain points and translating legacy technical debt into streamlined digital products.

Nice-to-Have / Added Advantages

• Prior experience building source-to-source compilers, automated code conversion utilities, or automated design document generators.
• Familiarity with software architecture patterns, legacy enterprise frameworks, and multi-language code conversion strategies.
• Knowledge of quantitative product analytics to track AI feature adoption, model accuracy, and user efficiency improvements.
• An active GitHub profile or portfolio demonstrating end-to-end AI applications, open-source contributions, or custom LLM tooling.

Success Metrics

• Product Efficiency & Impact: Quantifiable reduction in manual technical debt and turnaround time for automated code and document generation tasks.
• System Scalability & Reliability: High uptime, low inference latency, and robust error handling across production AI pipelines on GCP.
• Innovation & Quality: Successful deployment of high-accuracy AI models that consistently outperform baseline metrics in complex, domain-specific tasks.
• Collaboration & Delivery Efficiency: Timely feature releases, clean API handoffs, low rework rates, and strong cross-functional alignment throughout the product lifecycle.

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.

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At Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.