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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Machine Learning Principal Engineer - **Company:** Amadeus IT - **Location:** Nice, France - **Contract:** Permanent contract - **Skills:** Clean Code Principles, A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amadeus CRS, Continuous Integration, Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, Software Engineering, Data Logging, Large Language Models, Prompt Engineering, Generative AI, Information Technology, Machine Learning Operations, Virtual Agents, Marketplace - **Published:** June 7, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=768ac4f0676272cf ## About the Role Do you have experience in Python?, * Several years of software engineering experience, with significant hands-on work building and deploying ML or AI systems in production * Strong technical foundation in computer science, applied mathematics, or a related field * Deep, production-level proficiency in Python * Hands-on experience with LLM application development: prompt engineering, retrieval-augmented generation (RAG), function calling, agent orchestration * Experience building and operating data and ML pipelines at scale - not just training models, but serving them reliably * Track record of operating in fast-moving environments - startup, incubation, or high-growth teams where ambiguity is the norm and shipping under pressure is expected * Strong software engineering fundamentals: clean code, testing, CI/CD, observability Nice to Have * Experience with travel, marketplace, or multi-sided platform domains * Familiarity with AI Assistant platform integration patterns (plugins, skills, function calling, MCP) * Experience with evaluation and quality frameworks for LLM-based systems (automated evals, RLHF, human evaluation pipelines) * Background in information retrieval, search, or recommendation systems Diversity & Inclusion ## Description Software Machine Learning Principal Engineer About the AI Delivery Hub The AI Delivery Hub is a key engine in Amadeus' transformation toward an AI-first organization. It brings together focused, multidisciplinary teams to design, build, and deliver AI products solutions end-to-end. Working closely with product and platform teams across different business lines, the Hub accelerates delivery and scales AI across Amadeus' portfolio by turning proven work into reusable capabilities and real product outcomes. By joining the AI Delivery Foundry, you'll be part of a core engineering and delivery team focused on turning successful AI initiatives into reusable building blocks that can be adopted across Amadeus' products. The team works closely with product teams, platform teams, and business lines to design, assemble, and industrialize AI components and workflows, helping accelerate delivery while ensuring consistency and scalability at global travel scale. Core Responsibilities * Design and deploy LLM-powered systems embedded either in AI Assistant travel workflows (from conversational planning to booking orchestration) or in all the critical use cases defined by the business lines * Build retrieval, orchestration, and evaluation pipelines * Improve reliability, quality, latency, and cost efficiency of AI-powered travel experiences in production * Develop and maintain evaluation frameworks - automated and human-in-the-loop - to measure answer quality, hallucination rates, and task completion across travel use cases * Partner closely with product to define feasible AI capabilities, translating user needs and platform constraints into technical designs * Instrument systems for observability and learning: logging, telemetry, A/B testing infrastructure to support rapid experimentation * Stay current with the fast-moving LLM and AI Agent ecosystem - models, tooling, integration patterns - and translate relevant advances into production improvements * Collaborate with Amadeus platform and data teams (Universal Distribution, Hospitality, Payments) to access, shape, and integrate the content and APIs that power the travel experience What Good Looks Like * Has shipped AI/ML systems used by real customers in production - not just research or prototypes * Understands model trade-offs and failure modes: knows when to use a frontier model vs. a fine-tuned smaller model, when to cache, when to fall back * Moves quickly without sacrificing judgment - comfortable making pragmatic engineering decisions under uncertainty * Thinks in systems, not just models: retrieval quality, orchestration reliability, latency budgets, cost per query all matter as much as model selection * Communicates clearly with technical and non-technical stakeholders - can explain a technical trade-off to a PM and a product constraint to an engineer ## Related Videos - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Launching a marketplace on-time: A lesson in taking shortcuts using spreadsheets!](https://www.wearedevelopers.com/videos/477-launching-a-marketplace-on-time-a-lesson-in-taking-shortcuts-using-spreadsheets) - [Would You Buy Your Own HR? A Product Mindset for People Leaders](https://www.wearedevelopers.com/videos/1874-would-you-buy-your-own-hr-a-product-mindset-for-people-leaders) - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) - [How to Monetize Your APIs](https://www.wearedevelopers.com/videos/749-how-to-monetize-your-apis) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)