About the opportunity

Role: Principal Data Scientist - GenAI, Recommendation & Personalization

Location: Bangalore

Experience Level: 8-10 years

About the function:

At MakeMyTrip (MMT), technology is at the heart of everything we do. As a leading player in the travel industry, we leverage cutting-edge solutions like AI, machine learning, and cloud infrastructure to create seamless and innovative travel experiences. We prioritize a tech-first mindset, where our engineering teams lead the charge in transforming the way people travel.

Our culture encourages collaboration, creativity, and thought leadership, empowering engineers to challenge the status quo and develop impactful, game-changing solutions. At MMT, you'll work with the latest technologies, continually push boundaries, and have the chance to shape the future of travel.

Join us and become part of a forward-thinking, tech-driven organization that thrives on innovation and continuous learning.

About the Role:

We are looking for a Principal Data Scientist to drive advanced AI/ML initiatives for large-scale consumer products while providing technical leadership to a team of Data Scientists. This is a hands-on technical leadership role for someone

with deep expertise across Generative AI, Large Language Models, Recommendation Systems, Personalization, Search, NLP, Information Retrieval, and Representation Learning. The successful candidate will remain deeply involved

in research, experimentation, modeling, and system design while leading the technical direction, architecture, and execution of complex AI/ML initiatives.

The role operates at the intersection of AI research and production engineering. The ideal candidate should be comfortable evaluating state-of-the-art research, designing novel ML architectures, running large-scale experiments, and translating successful approaches into reliable and scalable production systems.

In addition to hands-on technical contributions, the candidate will mentor Data Scientists, drive architecture and design reviews, establish ML best practices,



and help raise the overall technical bar of the Data Science organization.

What will you be doing:

- Architect, develop, and productionize large-scale Machine Learning, Deep Learning, and Generative AI systems for consumer-facing applications.
- Build personalization and recommendation systems using techniques such as Two-Tower architectures, neural retrieval, sequential modeling, representation learning, and learning-to-rank.
- Design, fine-tune, and post-train Large Language Models (LLMs) and Small Language Models (SLMs) using techniques such as Supervised Fine-Tuning (SFT), LoRA/PEFT, preference optimization, GRPO, RLHF, and RLVR.
- Design custom reward functions, reward models, and evaluation signals for reinforcement-learning-based LLM post-training, incorporating task-specific, quality, factuality, and preference signals.

Develop embedding and representation-learning systems using contrastive learning, self-supervised learning, sequence modeling, and related deep-learning techniques.

- Build semantic search, retrieval, and ranking systems

leveraging dense retrieval, vector search, multi-vector retrieval, reranking, and modern neural Information Retrieval techniques.

- Explore and develop RAG, Graph RAG, Knowledge Graph, Agentic AI, tool-calling, and multi-step reasoning systems for complex AI

applications.

- Build scalable synthetic-data generation, data augmentation, and model-training pipelines for LLM and ML applications.
- Design robust offline and online evaluation frameworks for Generative AI, retrieval, ranking, recommendation, and personalization systems,

including A/B experimentation.

- Optimize AI/ML systems across model quality, inference latency, throughput, scalability, reliability, compute, and serving cost.




- Translate ambiguous business problems into well-defined ML problems and independently drive them through problem formulation, research,

experimentation, architecture, productionization, and measurement.

- Partner closely with Product, Engineering, Analytics, and Data teams to deliver AI/ML capabilities with measurable customer and business impact.
- Provide technical leadership to a team of Data Scientists

through architecture reviews, technical guidance, mentoring, experimentation strategy, and ML engineering best practices.

- Continuously evaluate emerging AI/ML research and determine its applicability to real-world problems and production systems.

Qualification & Experience:

- 8+ years of industry experience in Machine Learning, Data Science, Applied AI, NLP, Information Retrieval, Recommendation Systems, or related areas.
- Strong hands-on expertise in Python and modern deep-learning frameworks such as PyTorch and/or TensorFlow.
- Deep understanding of Transformers, attention mechanisms, embeddings, representation learning, and modern deep-learning architectures.
- Robust expertise across multiple areas such as Generative AI/LLMs, Recommendation Systems, Personalization, Search & Ranking, NLP, Information Retrieval, and Representation Learning.
- Hands-on experience across the modern LLM lifecycle, including SFT, PEFT techniques such as LoRA, preference optimization, reward modeling, RL-based post-training approaches such as GRPO/RLHF/RLVR, evaluation, and inference.
- Experience designing and deploying production-grade ML systems at scale and working with large-scale behavioral, interaction, or transactional datasets.
- Strong system-design skills with the ability to make practical trade-offs across model quality, latency, compute, scalability, reliability, and cost.
- Demonstrated ability to provide technical direction and mentor Data Scientists and ML Engineers.
- Strong communication and collaboration skills, with the ability to work effectively across Product, Engineering, Data, Analytics, and business stakeholders.

📌 Principal Data Scientist (Bengaluru)
🏢 the function
📍 Bengaluru