Data Science Engineer 3

Job Title

Business Environment

Joining Amadeus means shaping the technology that keeps airports and airlines moving for millions of travelers every day. Our department, Airport and Airline Operations (AirOps), is the leading industry platform in airline and airport operations, delivering seamless and personalized passenger experience, an optimized airport operation, and automated disruption resolution between airlines, airports, border, and air traffic control.

Within AirOps Engineering in Bangalore (TRU-AOP-ENG), the FBR team [Flight Management, Baggage Reconciliation and Ramp operations] works closely with engineering, product and data colleagues across Amadeus sites worldwide. It delivers solutions for efficient and optimized airport and airline operations.

In FBR you will work on AI solutioning that has a visible impact on airline and airport operations.

· Meaningful work applying generative and agentic AI to real operational challenges in the travel industry.

· Modern, cloud-native technology and the chance to influence architecture and engineering practices.

· Continuous learning, with exposure to emerging AI technologies and opportunities to grow your expertise.

· A collaborative, inclusive culture that values diversity, flexibility and work-life balance.

Summary of the Role : AI Engineer

As an AI Engineer in FBR, you will join a multidisciplinary team of software engineers, data engineers, data scientists, product specialists and designers building the next generation of AI-powered solutions for airport and airline operations. The team develops cloud-native services with modern frontend and backend technologies, with a strong focus on production-grade AI agents and AI advisors.

You will combine deep expertise in generative and agentic AI with solid machine learning and MLOps foundations to design, develop and deploy scalable, AI-driven solutions. Working autonomously within defined processes, you will turn real business problems into reliable, secure and cost-efficient AI capabilities that operate at production scale.

Your Main Responsibilities

· Own the design, development, deployment and maintenance of GenAI-powered solutions that support analytics and aviation operations, including new AI accelerators.

· Implement and produce GenAI and agentic workflows using cloud-native technologies, primarily Microsoft Azure within a multi-cloud strategy, integrating them with existing data platforms and operational systems.

· Build and maintain scalable APIs and backend services for conversational interfaces, tool-calling agents and AI-assisted analytics features, with close attention to latency, cost and reliability.

· Design and develop scalable AI/ML solutions for real business problems, building and deploying predictive and optimization models on structured and unstructured data.

· Drive the end-to-end ML lifecycle, from data preparation, training and validation through deployment and monitoring, ensuring model performance, scalability and reliability.

· Apply Responsible AI principles throughout delivery, including security, data privacy, compliance and transparent handling of model limitations.

· Conduct code reviews, mentor junior engineers and share best practices across the team.

· Stay current with advances in AI/ML and generative AI, and drive innovation by evaluating new techniques and tools for FBR use cases.

· Deliver within Agile/SAFe ways of working, adapting to changing customer-driven needs.

Technical Responsibilities

· Design and orchestrate agentic AI systems (for example with LangGraph or Agent Development Kit), including tool calling, memory and multi-step reasoning, and the supporting infrastructure from concept to deployment.

· Contribute to the design and optimization of AI context management, covering embeddings, retrieval (RAG), prompt construction and caching, so that AI experiences are fed with reliable, compliant data.

· Establish LLM evaluation frameworks and guardrails, including offline and online evaluation, completeness and safety checks, and iterative improvement driven by real usage.

· Optimize cost and latency of AI workloads through model selection, token and context budgeting, caching and efficient serving patterns.

· Build and integrate data pipelines and deploy ML/DL models (regression, classification, clustering, NLP, deep learning) with TensorFlow, PyTorch or Scikit-learn into production systems.

· Implement MLOps and LLMOps practices: CI/CD for ML, experiment tracking, model versioning, reproducibility, and appropriate evaluation metrics and tuning.

· Ensure end-to-end observability of AI services through logging, tracing, monitoring and alerting, and troubleshoot data and model issues in production.

· Leverage Azure (and where relevant AWS or GCP) for AI/ML workloads, using serverless containers, event-driven architectures and API gateways.

Stakeholder Collaboration & Influence

· Collaborate with Product Owners, Data Engineers and cross-functional product, engineering and business teams.

· Translate business problems into data-driven AI solutions with clear success criteria.

· Communicate insights, model outcomes and technical decisions clearly to technical and non-technical audiences.

· Build alignment across stakeholders through data-backed approaches.

About the Ideal Candidate

Must have

· Bachelor's or Master's degree in Computer Science, Data Science or a related field.

· 6+ years of experience in AI/ML development and deployment.

· Hands-on experience shipping GenAI solutions to production: prompt design, orchestration, RAG (e.g. Azure AI Search), evaluation frameworks, monitoring and iterative improvement from real usage.

· Experience designing and orchestrating agentic AI systems (e.g. LangGraph, Agent Development Kit) from concept to deployment.

· Strong Python (preferred) or Java/Scala, with good engineering practices: testing, code reviews and documentation.

· Solid grounding in ML frameworks (TensorFlow, PyTorch, Scikit-learn), statistics, probability and data modeling.

· Experience with large datasets and distributed computing.

· Solid cloud experience, preferably Azure (serverless, containers, event-driven architectures, API gateways, logging and monitoring), and MLOps practices.

· Working knowledge of Responsible AI, security and data-privacy requirements for AI systems.

Nice to have

· Aviation, airline or airport operations domain knowledge.

· Experience with Azure OpenAI and Azure AI Foundry.

· Familiarity with Model Context Protocol (MCP) and tool-calling patterns.

· Experience with vector databases.

· Containerization and orchestration with Docker and Kubernetes.

· Azure AI certifications.

· Experience working in Agile frameworks (Scrum, SAFe).

Soft skills

· Strong problem-solving skills and the ability to work autonomously within defined processes.

· Clear, confident communication in English, both written and verbal.

· Thrives in a multi-cultural, global organization and adapts readily to changing, customer-driven needs.

· Collaborative mindset, curiosity and a willingness to mentor and share knowledge.

Diversity & Inclusion

Amadeus aspires to be a leader in Diversity and Inclusion in the tech industry, enabling every employee to reach their full potential by fostering a culture of belonging and fair treatment, attracting the best talent from all backgrounds, and as a role model for an inclusive employee experience.

Amadeus is an equal opportunity employer and committed to an accessible recruitment process. All qualified applicants will receive consideration for employment without regard to gender, race, ethnicity, sexual orientation, age, beliefs, disability or any other characteristics protected by law.

Be aware of recruitment scams

Amadeus Group never charges fees, requests payment, or asks for financial information during recruitment. All legitimate opportunities are communicated solely through official Amadeus channels, including our careers website. Any payment request or outreach via unofficial platforms (e.g., WhatsApp, Telegram) should be treated as fraudulent.