AI Engineer

Job description

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.

Company

Airbus India Private Limited Employment Type: Permanent ------- Experience Level: Professional Job Family: Digital By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus. Airbus is committed to achieving workforce diversity and creating an inclusive working environment.

We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief. Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to [email protected] . 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.