Manager of Artificial Intelligence

Job Description 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 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: 710 years in software/AI engineering, including 35 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. Application Process As part of the hiring process, candidates will complete one of two production-inspired AI architecture case studies. The assessment evaluates engineering judgment, system design approach, and the ability to build production-ready AI solutions both the architectural thinking and the execution detail. A working implementation, GitHub repository, and a short arch .