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Metro location and popular ML title increase competition, but specialized OR and MLOps skills limit applicants.
Core ML/AI and optimization skills are transferable, though travel domain knowledge is desirable but optional.
Explicit seven-year minimum, PhD preference, and mandatory ML, OR, and MLOps skills enforce strict filtering.
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Architect, develop, and deploy advanced AI and optimization software integrating machine learning and mathematical models into travel retail, network planning, and agency automation platforms.
Lead R&D by prototyping and scaling AI solutions such as Large Language Models for automated agency support and real-time itinerary optimization in travel.
Define and enforce AI lifecycle and MLOps standards; migrate legacy automation to cloud-based (GCP) AI architectures; mentor junior data scientists and engineers.
Minimum 7 years of experience in Data Science, AI Engineering, or Operations Research.
Advanced degree required (PhD preferred) in Mathematics, Statistics, Computer Science, or Physics with research and innovation track record.
Expertise in optimization modeling (LP, MIP) and solvers like Gurobi or CPLEX.
Strong programming skills in Python and C++ plus experience with cloud AI platforms (GCP, Vertex AI), MLOps, containerization (Docker, Kubernetes), and generative AI frameworks (e.g., LangChain, LlamaIndex).
Experienced leader capable of independently driving complex, cross-functional AI engineering projects from research to production in global, high-availability travel environments.
Deep domain knowledge or ability to work with airline operations, network planning, departure control systems, or travel data ecosystems (GDS, OTA).
Technical excellence in advanced machine learning techniques (Deep Learning, Reinforcement Learning, NLP), high-performance computing, and scalable AI system design on cloud-native platforms.