AI Engineer at Airbus in Bangalore Area

Job description

• Role : AI Engineer
• 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.

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.

Requirements

• 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.
• 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).
• 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.
• 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.

Skills

Required

• Google Cloud Platform (GCP)
• Vertex AI
• Cloud Run
• BigQuery
• Cloud Functions
• GKE
• LLMs
• RAG architectures
• LangChain
• LlamaIndex
• Python
• RESTful APIs