• Define the long-term AI roadmap: You won’t just follow trends; you’ll decide how Machine Learning and AI can uniquely solve travel pain points.

• Build & Scale: Lead a world-class team of Applied Scientists and ML Engineers.

• Consumer Impact: Deeply understand consumer behavior to build models that feel like magic to the user.

• Cross-Pollination: Partner with Product and Engineering to ensure AI isn't a silo, but a horizontal layer that enhances every click on the ixigo, ConfirmTkt, and AbhiBus apps.

Your DNA

• The Scientist-Builder: You have a PhD or Master's in CS/AI/Math, but you also have "calluses on your hands" from shipping real products. You know that a model is only as good as the problem it solves.

• LLM Native: You live and breathe Transformers. You understand the nuances of prompt engineering, fine-tuning, and the infrastructure required to serve large models at scale.

• The Scale Expert: You’ve handled datasets that would make a regular laptop melt. You are comfortable with distributed computing, MLOps, and optimizing models for mobile-first environments.

• Strategic & Witty: You can explain complex neural networks to a boardroom and the business value of a gradient descent to a developer—and you might even make a joke about it in the process.

Technical Requirements 

1. Foundation Models & Generative AI

• Deep expertise in traditional and foundational Machine learning techniques and models.

• Extensive experience with Deep learning architectures, fine-tuning, and efficient adaptation techniques.

• Proven ability to build autonomous agents capable of multi-step reasoning and tool-based execution.

• Expertise in building and deploying high-concurrency models at scale.

• Mastery of production-grade retrieval systems and vector database management.

• Experience with Vision-Language Models for automating document parsing and voice-interface models for vernacular language support.

2. Architecture & MLOps

• Experience with high-throughput, low-latency serving frameworks.

• Ability to architect AI-first microservices that handle hundreds of millions of requests monthly while managing compute costs.

3. Academic & Professional Pedigree

• Education: MS or PhD in Computer Science, AI, Mathematics, or a related quantitative field.

• Experience: 10+ years in technology, with at least 5+ years leading high-performance AI research or applied science teams in a fast-paced consumer tech environment.

• The "Builder" Portfolio: A portfolio of shipped products is preferred over a portfolio of just research papers. We value "Production-ready Research.