Mondee - Artificial Intelligence Architect - Tabhi

Job location: [{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Hyderabad","addressCountry":"IN"}}] Work arrangement: Employment type: FULL_TIME Salary: "" Experience requirements: {"monthsOfExperience":84,"educational":{"credentialCategory":"bachelor degree"}} Education requirements: "" Job Title : AI Architect Founder & CEO's Office | Hyderabad (On-site) | Full-time About Tabhi (The Parent Company of Mondee) : Tabhi is a $4B AI-native travel company and the world's largest AI-first travel platform, revolutionizing travel, tourism, and experiential services through three integrated, AI-powered verticals. The Tabhi Group of Companies : - 23 Companies - 3 AI Platforms - One Vision - Mondee (B2B) The Agentic AI Travel Marketplace - Miraee (B2E) The Next-Gen Employee Travel Platform - Abhee (B2C) The Hyperlocal Experiential Marketplace Our Scale : - 65K+ Global Customers - 500+ Airline Partners - 2M+ Hotels & Vacation Rentals - 50M Shopper Searches Per Day - 125M+ Consumers Access The Opportunity : We are seeking AI Architect(s) to join the Founder & CEO's Office to design and deliver the next generation of production-grade AI systems across the Tabhi Group. This is a hands-on architecture role. The AI Architect is accountable both for the system-level view architecture, scalability, governance, and business impact and for the engineering detail required to complete each build cycle and deliver the product: retrieval design, evaluation, deployment, monitoring, and cost control. Architecture at Tabhi is measured by shipped, operating software. Key Responsibilities : - Design and build production-ready agentic AI systems, owning them from architecture through deployment, monitoring, and iteration. - Define reference architectures, technology selections, and integration patterns for AI systems across the three platforms. - Architect scalable AI infrastructure and intelligent workflows, including multi-agent orchestration and human-in-the-loop processes. - Build and maintain RAG pipelines, LLM integrations, and evaluation harnesses in production codebases. - Establish and enforce standards for responsible AI - guardrails, AI governance, data security, and model evaluation. - Own production reliability across the stack - agent failures, retrieval quality degradation, latency, and cost regressions. - Collaborate with product and engineering teams to translate complex business problems into working systems. - Shape the technical direction of AI across the organization, working directly with leadership. Required Qualifications : - Experience : 7-10 years in software/AI engineering, including 3-5 years of hands-on experience delivering production machine learning or generative AI systems. - Programming : strong, current Python with a record of owning production code; solid software-engineering fundamentals including API design and distributed systems. - Agentic AI and LLMs : hands-on experience with LLM-based and autonomous agent systems, including multi-agent frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen. - RAG and retrieval : production experience designing RAG architectures, embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus). - Prompt and context engineering : demonstrated ability to design prompts, context strategies, and evaluation methods that perform reliably at production scale. - Cloud AI platforms : deep experience with at least one major cloud AI stack - Azure OpenAI, AWS Bedrock/SageMaker, or Google Vertex AI - plus familiarity with data platforms such as Databricks or Snowflake. - MLOps/LLMOps : CI/CD for models and prompts, containerization with Docker/Kubernetes, observability/monitoring, and cost-performance optimization in production. - Responsible AI : working knowledge of AI governance, guardrails, security, and data-quality standards. - Communication : demonstrated ability to lead technical design reviews with engineers and present architecture decisions and trade-offs to executive stakeholders. - Education : Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience; Master's degree preferred. Preferred Qualifications : - Experience with agent reasoning patterns such as ReAct, Plan-and-Execute, Reflection, and Tree-of-Thought. - Model fine-tuning experience (e.g., LoRA/PEFT) and familiarity with knowledge graphs. - Experience with ML frameworks (PyTorch, TensorFlow) and experiment/model management (MLflow). - Cloud architecture certification (Azure Solutions Architect Expert, AWS Solutions Architect Professional, or equivalent). - Experience building consumer- or marketplace-scale systems in travel, e-commerce, or similar high-volume domains.