ob Title
: AI/ML Architect
Overall Experience
: 8-10 Years
Relevant Experience
: 4–6 Years
About the Role:
We are looking for an experienced AI/ML Engineer 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:
Personalized recommendations (hotels, destinations, packages)
Price prediction and demand forecasting
Route optimization (multi-city travel planning)
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:
Generative AI (LLMs for travel assistants)
RAG-based systems for conversational search
Vector search and similarity-based retrieval
Required Skills & Qualifications:
4–6 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.
Regression, Classification, Clustering
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.
