Artificial Intelligence Architect
First seen by Alion on Aug 13, 2026.
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.
Skills
Artificial Intelligence, Technical Architect, Generative AI, Gen AI Strategy, AI Governance, AI Project Management, AI Strategy, Agentic AI, Machine Learning, Large Language Model