We are looking for an experienced AI/ML Architect to join our travel technology platform and drive intelligent, data driven experiences.

You will play a key role in building smart systems such as personalized travel recommendations, dynamic pricing, route optimization, and conversational AI assistants, coding assistants and other AI Initiatives at organization.

This role is ideal for someone who has hands on experience in at least one end to end AI/ML project and is passionate about applying machine learning to real world business problems.

Key Responsibilities :

- Design, develop, and deploy scalable machine learning models for travel & retail use cases such as :

a. Personalized recommendations (hotels, destinations, packages).

b. Price prediction and demand forecasting.

c. Route optimization (multi-city travel planning).

d. Customer segmentation and behavior analysis.

- Build and maintain end to end ML pipelines (data ingestion ? preprocessing ? training ? evaluation ? deployment).

- Work with large datasets (structured and unstructured) including user behavior, bookings, and search data.

- Integrate ML models into production systems via APIs and microservices.

- Collaborate with product, engineering, and business teams to translate requirements into ML solutions.

- Implement experimentation frameworks (A/B testing) and continuously improve model performance.

- Explore and integrate modern AI approaches such as :

a. Generative AI (LLMs for travel assistants).

b. RAG-based systems for conversational search.

c. Vector search and similarity-based retrieval.

Required Skills & Qualifications :

- 4 to 6 years of experience in AI/ML or Data Science roles.

- Experience working with Google CX Agent.

- Experience working with Google Vertex ai and vector database.

- Experience working with Google Commerce search.

- Strong hands-on experience in Python (NumPy, Pandas, Scikit-learn).

- Experience with at least one end-to-end ML project in production.

- Solid understanding of ML algorithms :

a. Regression, Classification, Clustering.

b. Recommendation Systems (Collaborative/Content-based).

- Experience with model deployment (Flask/FastAPI, Docker, APIs).

- Good understanding of data structures, algorithms, and statistics.

- Experience working with databases (SQL/NoSQL).

- Understanding of models like co-sin similarity etc.

- Understanding coding agents architecture and implementation.