Job
Description
About Holiday Tribe Holiday Tribe is a Great Place To Work® Certified™, seed-stage VC-funded travel-tech brand based in Gurugram. We specialize in crafting unforgettable leisure travel experiences by integrating advanced technology, leveraging human expertise, and prioritizing customer success. With holidays curated across 30+ destinations worldwide, partnerships with renowned tourism boards, and recognition as the Emerging Holiday Tech Company at the India Travel Awards 2023, Holiday Tribe is transforming the travel industry. Our mission is to redefine how Indians experience holidays making travel planning faster, smarter, and more personalized, ensuring every trip is truly seamless and unforgettable. The Role We are seeking a talented AI Engineer to join our growing engineering team and play a pivotal role in building our AI-first travel platform. You will be responsible for designing, developing, and deploying AI systems that power personalized travel recommendations, intelligent itinerary generation, and our innovative sales assistant tools. This is a unique opportunity to shape the future of travel technology while working with state-of-the-art AI capabilities. Key Responsibilities: AI System Development Design and implement Retrieval Augmented Generation (RAG) systems for travel recommendation and itinerary planning Build and optimize large language model integrations using frameworks like LangChain for travel-specific use cases Develop semantic search capabilities using vector databases and embedding models for travel content discovery Create tool-calling architectures that enable AI agents to interact with booking systems, inventory APIs, and external travel services Implement intelligent conversation flows for customer interactions and sales assistance Travel Intelligence Platform Build personalized recommendation engines that understand traveler preferences, seasonal factors, and destination characteristics Develop natural language processing capabilities for interpreting customer travel requests and preferences Implement real-time itinerary generation systems that consider multiple constraints (budget, time, preferences, availability) Create AI-powered tools to assist travel experts in creating customized packages faster Build semantic search engines for finding relevant travel content based on user intent and contextual understanding AI Agent & Tool Integration Design and implement function calling systems that allow LLMs to execute actions like booking confirmations, inventory checks, and pricing queries Build multi-agent systems where specialized AI agents handle different aspects of travel planning (accommodation, transportation, activities) Create tool orchestration frameworks that enable AI systems to chain multiple API calls for complex travel operations Implement safety and validation layers for AI-initiated actions in critical systems Data & Model Operations Work with travel knowledge graphs to enhance AI understanding of destinations, accommodations, and activities Implement hybrid search systems combining semantic similarity with traditional keyword-based search Build vector indexing strategies for efficient similarity search across large travel content databases Implement model evaluation frameworks to ensure high-quality AI outputs Optimize AI model performance for cost-efficiency and response times Collaborate with data engineers to build robust data pipelines for AI training and inference Cross-functional Collaboration Partner with product teams to translate travel domain requirements into AI capabilities Work closely with backend engineers to integrate AI services into the broader platform architecture Collaborate with UX teams to design intuitive AI-human interaction patterns Support sales and customer success teams by improving AI assistant capabilities Required Qualifications: Technical Skills 3+ years of experience in AI/ML engineering with focus on natural language processing and large language models Strong expertise in RAG (Retrieval Augmented Generation) systems including vector databases, embedding models, and retrieval strategies Hands-on experience with LangChain or similar LLM orchestration frameworks, including tool calling and agent patterns Proficiency with semantic search technologies including vector databases, embedding models, and similarity search algorithms Experience with tool calling and function calling in LLM applications, including API integration and action validation Proficiency with major LLM APIs (OpenAI, Anthropic, Google, etc.) and understanding of prompt engineering best practices Experience with vector databases such as Milvus, Weaviate, Chroma, or similar solutions Strong Python programming skills with experience in AI/ML libraries (transformers, sentence-transformers, scikit-learn) AI/ML Foundation Solid understanding of transformer architectures, attention mechanisms, and modern NLP techniques Deep knowledge of embedding models and semantic similarity techniques (sentence transformers, dense retrieval methods) Experience with hybrid search architectures combining dense and sparse retrieval methods Knowledge of fine-tuning approaches and model adaptation strategies Understanding of agent-based AI systems and multi-step reasoning capabilities Understanding of AI evaluation metrics and testing methodologies Familiarity with MLOps practices and model deployment strategies Software Engineering Experience building production-grade AI applications with proper error handling and monitoring Experience with API integration and orchestration for complex multi-step workflows Understanding of API design and microservices architecture Familiarity with cloud platforms (AWS, GCP, Azure) and their AI/ML services Experience with version control, CI/CD, and collaborative development practices Preferred Qualifications: Advanced AI Experience Experience with multi-modal AI systems (text, images, structured data) Advanced knowledge of agent frameworks (LangGraph, CrewAI, AutoGen) and agentic workflows Experience with advanced semantic search techniques including re-ranking, query expansion, and result fusion Experience with model fine-tuning, especially for domain-specific applications Knowledge of tool use optimization and function calling best practices Understanding of AI safety, bias mitigation, and responsible AI practices Technical Depth Experience with advanced RAG techniques (hybrid search, re-ranking, query expansion, contextual retrieval) Knowledge of vector search optimization including indexing strategies, similarity metrics, and performance tuning Experience building tool-calling systems that integrate with external APIs and services Knowledge of graph databases and knowledge graph construction Familiarity with conversational AI and dialogue management systems Experience with A/B testing frameworks for AI systems
