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Metro location, mid-level ML role, broad tech stack, and known employer increase candidate competition.
Core ML and MLOps skills are transferable, but travel/GDS domain knowledge raises domain specificity moderately.
Explicit 4+ years plus mandatory ML/AI, MLOps, GCP, and high-performance programming requirements make screening strict.
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Architect, develop, and deploy advanced data intelligence pipelines integrating mathematical algorithms and ML models into core travel platforms including retailing, Network Planning, and Departure Control.
Drive research and innovation by prototyping and scaling AI-driven solutions such as real-time itinerary optimization, TMC automation, and conversational commerce.
Define AI lifecycle policies and promote engineering best practices to ensure high-fidelity, production-grade ML systems operating at scale in travel environments.
Minimum 4 years of experience in Data Science, AI Engineering, or Operations Research.
Advanced degree (Masters or PhD preferred) in Mathematics, Machine Learning, Statistics, Computer Science, or Physics with a research background.
Expertise in ML/AI including Deep Learning, Reinforcement Learning, NLP, and proficiency in Python, golang, or C++ for performance-sensitive applications.
Experience with modern AI tech stack including GCP (Vertex AI, BigQuery), MLOps tools, containerization (Docker, Kubernetes).
Experience delivering production-grade AI/ML solutions in complex, high-volume travel or related domains such as GDS or OTA systems.
Proven ability to translate research prototypes into scalable, low-latency systems with strong software engineering discipline.
Comfortable leading cross-functional teams and influencing stakeholders on adoption of disruptive AI technologies in travel retail and operations environments.