Senior AI Architect / AI Engineering Team Required – Book With AI (Phase 2)

@milanrathod945

Multi-agent pipelines fall over when handoffs leak state, structured typed handoffs with per-stage evaluation stop that, and I've shipped a 12-agent pipeline that holds. For Book With AI, I’d architect a multi-agent system with structured handoffs between agents handling flight logic, conversational memory, and API integrations, ensuring state integrity and evaluation at each stage. This mirrors the 12-agent stock analysis pipeline I built, which produces BUY/SELL/HOLD decisions with entry, target, and stop-loss levels. I’ll deliver the AI orchestration architecture, conversational memory framework, and API orchestration framework within 3 weeks. Can you share the API docs for Amadeus and Skyscanner so I can start mapping integrations?

@kamran2012

Interesting project, I will architect the Phase 2 AI orchestration layer — multi-agent travel reasoning, conversational memory framework, and real-time API orchestration across Amadeus, Skyscanner, and hotel APIs. For complex multi-city itineraries, I will design a LangGraph-based agent graph where specialized agents handle fare logic, layover validation, and date optimization independently, then a coordinator agent merges results. This avoids monolithic prompt chains that break under complex travel conversations and scales well toward your 300K+ daily visitor target. Questions: 1) What LLM provider and vector database did Phase 1 establish — or is the stack open for Phase 2? 2) How is conversational memory currently handled — session-based, or do you already have persistent user memory in place? Looking forward to your response. Best regards, Kamran

@Mahihassi

As an AI and Cloud Developer and a seasoned professional in the field, I have been fortunate to work on a variety of projects similar to yours that call for a comprehensive understanding of chatbot development, AI model development, API development, API integration, and Conversational AI۪. For your project's Phase 2 requirements, which include managing complex travel conversations along with multiple-city itineraries, applying airline and ticketing logic efficiently, and quickly processing real-time travel data from various APIs, my expertise in building scalable backend architecture robust AI Workflow Orchestration can play a decisive role. In terms of tech stack, I leverage Python, FastAPI, Node.js, React, cloud platforms like AWS/Azure/GCP and use database systems like MongoDB /Redis to ensure high scalability, fault-tolerant systems for your enterprise-grade needs. My experience also includes utilizing AI services such as OpenAI APIs and LLMs which would be beneficial for this project. In addition to this, I have hands-on experience with technologies like LangGraph, LangChain,LlamaIndex along with Weaviate or Qdrant which are similar to what you're seeking for your project. This not only adds experience to my portfolio but also significantly reduces risk and improves turnaround time.

@Hrolgar

Flight prices change faster than any vector index can keep up with. The core architectural decision for Phase 2 is knowing exactly where RAG stops and live API calls begin. Static route intelligence (hubs, typical layovers, airline quirks, policy data) belongs in the vector store. Real-time fare availability belongs in a tool-call to a GDS or aggregator. Getting that boundary wrong means your conversational layer will confidently quote prices that expired before the user hits confirm. The approach I'd take: a two-layer retrieval stack in Python, with a lightweight agent orchestrator (function-calling LLM as the router) sitting between the user intent layer and both the vector DB and the live flight APIs. The agent decides per query whether to pull from indexed route/policy context, call the live fare endpoint, or both, then synthesizes a grounded response. Prompt engineering here is mostly about the tool-selection logic and constraining the model on what it cannot make up (prices, seat counts, availability). For the vector DB, the right pick depends on your current infra: pgvector is the lowest-ops option if you're already on Postgres; Qdrant or Weaviate if you need horizontal scale or a standalone service. Worth confirming before committing to either. M1: Architecture design, vector DB setup, data ingestion pipeline. 35,625 INR, 5d. M2: Agent orchestrator, tool-calling layer, live flight API integration. 35,625 INR, 6d. M3: Conversational dialogue layer, multi-turn context management, session state. 35,625 INR, 5d. M4: End-to-end integration, load testing, deployment docs, handoff. 35,625 INR, 5d. This is an indicative estimate from the brief; I'll give you a firm quote once the scope is locked. What's the current state of Phase 1: is there an existing API layer I'd be building on, or is the flight data pipeline also coming together in parallel?

@sarimshoaib1425

AI agents specialist here, read through your full Phase 2 proforma and the architecture vision is clear, agentic orchestration with long conversational memory, multi-API travel logic, RAG and vector search for recommendations, all running as a deterministic travel operating system rather than a chatbot. For a project at this scale I'd suggest starting with one core component as an initial milestone, the conversational memory and context management layer using LangGraph with a vector database like Qdrant, since that's the foundation everything else builds on. I'd architect it modular from day one so the itinerary orchestration, travel logic layer and API integrations can be added incrementally without rework. I work daily with OpenAI APIs, agentic frameworks, tool calling and API orchestration including retry and rate-limit handling, exactly the patterns this needs. Let's talk.

@rakhshanz3

With a solid educational background in AI and 20 years of professional experience, I believe I am the ideal candidate to serve as your Senior AI Architect for your Book with AI Phase 2 project. My extensive knowledge covers all the key areas you've mentioned, from RAG to Context Management, or from Function/Tool Calling to Conversational Memory Systems. Moreover, my time at AI start-ups has proven that I possess the skills necessary to navigate travel-tech intricacies, an advantage your project seeks. My career has been marked by meaningful contributions to several multinational software firms and culminated in my role as Chief Technology Officer of an AI start-up. In this role, I steered the entire technical development process and successfully built advanced digital solutions, some of which share the challenges and ambition of your Book with AI. Embarking upon Phase 2 signifies your project's transition from exceptional to extraordinary - and that is exactly what I’m here to facilitate. I assure you impeccable services such as building the Conversational Memory Framework, Travel Intelligence Engine, or API orchestration framework - all in consonance with your long-term vision of creating a travel operating system supporting 300k+ daily visitors with enterprise-grade efficiency.

@Ritesh11gupta

Your project on transforming flight booking with AI sounds exciting! I have solid experience with API integration, which could really help in phase 2. What’s your vision for the user experience in this new platform?

@zain556

Can you share more details about the specific APIs you plan to integrate into the platform? This insight can help tailor the architecture to ensure a seamless experience for users. As an experienced AI architect with a strong background in scalable AI system design and travel tech, I have successfully deployed projects involving OpenAI APIs, multi-agent systems, and vector databases. My expertise also includes developing conversational memory systems and API orchestration for large-scale applications. My technical proficiency covers prompt engineering, RAG, and cloud deployment architecture, ensuring your platform can handle high volumes of conversational traffic and real-time travel intelligence. I am committed to creating a production-grade AI travel operating system that meets your complex travel conversation and workflow requirements. I look forward to discussing my relevant projects and how we can achieve your long-term vision for Book With AI.

@ammarahmed20

With over 6 years of experience in building and deploying production-grade AI systems, including complex conversational AI like the one required for your travel intelligence platform, I am confident in my ability to deliver on Phase 2 of Book With AI. Not only do I bring in-depth knowledge and expertise with OpenAI APIs and LLMs - essential for your project - but I am also well-versed in all the tools and technologies you've mentioned. What sets me apart from others is that I don't just build for demos; I build for real-world usage. This means that not only will my AI understand complex travel conversations and maintain long conversational memory, it will also be fully capable of handling multi-city itineraries, applying airline logic, and providing intelligent recommendations. 

@CauaAlves

I can architect and deliver the core intelligent systems required for Phase 2 of your travel platform. My focus will be on building a robust, scalable foundation that handles complex travel conversations, maintains context across multi-step interactions, and applies real travel logic (itineraries, fare rules, connections, and recommendations) in a reliable way. The solution will include a well-structured orchestration layer capable of coordinating multiple data sources and APIs, combined with a retrieval system that grounds responses in accurate travel information. I will also design the memory and context management approach to support longer, more natural travel planning sessions. I bring strong experience in designing production-grade backend systems that integrate external APIs cleanly, handle rate limits and fallbacks, and maintain high reliability under load. I am ready to define a clear architecture, prioritize the most critical components, and deliver working foundations that can evolve into a full production system. I am also comfortable working closely with your existing Phase 1 codebase. If selected, I will provide a detailed technical approach and recommended priorities for the initial delivery.

@dushyant317

We can architect and scale Book With AI into a production-grade AI travel intelligence platform using multi-agent AI, RAG, long-term memory, travel-specific decision engines, and real-time API orchestration designed for enterprise-scale traffic and complex itinerary planning.

@mengw3

Hi - this maps directly to what I build: production agentic AI with RAG, conversational memory and tool calling on the OpenAI/Claude APIs. Directly relevant to Phase 2: - Agentic/multi-agent orchestration with function/tool calling - the right pattern for multi-city itineraries, fare/ticketing logic and real-time travel-API calls as tools. - RAG + vector DBs (Pinecone/Weaviate/Qdrant/pgvector) with hybrid search and reranking for airport/airline/route intelligence. - Long conversational memory + context management (summarized + retrieved) so the assistant tracks a complex trip across many turns. - Prompt engineering, structured outputs and guardrails, plus eval loops for accuracy/hallucination/latency/cost - what takes it from demo to production. - Orchestration with LangGraph/LangChain/LlamaIndex; FastAPI microservices and scalable inference. I'm a senior AI engineer (~20y) - I can architect this and scale to a small team if you prefer. Two questions: (1) Are the LLMs fixed (OpenAI only, or open to Claude/others)? (2) What's already built in Phase 1 for memory and the vector store, so I extend rather than duplicate? Profile: https://www.freelancer.com/u/mengw3

@sgwtechnology

I can architect and build your AI-native travel intelligence platform with scalable LLM orchestration, conversational memory, RAG, and multi-agent workflows. I’ll design a production-grade system integrating Amadeus, Skyscanner, Maps, and other travel APIs with robust fallback, rate-limit handling, and data normalization. The solution will support complex travel reasoning, multi-city itineraries, and high-volume real-time usage with enterprise scalability.

@hamzaalzuhair

The fastest way Phase 2 breaks at 300k daily visitors is letting the LLM reason over fare rules, baggage and layover validation directly. Those are deterministic, they belong in a logic layer the agent calls, not in the model's context where they bloat tokens and hallucinate. Approach: - LangGraph multi-agent orchestration (search, itinerary, fare/rules, recommendation) over one overloaded agent - Memory split into short-term context + long-term vector store (Qdrant/pgvector) with rolling summarization, so multi-city threads don't blow the token budget - Travel logic as a deterministic tool layer (fare rules, baggage, transit checks, alt airports, date optimization) called via tool calling - API orchestration with normalization, retry/fallback and rate-limit handling across Amadeus, Skyscanner, Maps Built on this exact pattern across 70+ AI agent systems, RAG pipelines and multi-agent finance builds. Close parallel: a calendar agent that validates a slot and offers alternatives when unavailable, same shape as your alt-airport and date logic. Team: I architect and own the build, pull in support as scope demands. Stack: LangGraph + OpenAI, Qdrant/pgvector, Supabase/Postgres, horizontal scale. Timeline: working foundation in ~3 weeks, then iterate. Budget: fix a first phase in range, prove the architecture before scaling spend. Free for a call to walk through it? Regards, Hamza

@serviceaxisgroup

Hi, I can help with a production-grade AI travel operating system that handles complex multi-city conversations, long conversational memory, and real-time flight/travel recommendations with your airline and ticketing logic. I’ll start by mapping Phase 2 workflows end-to-end (tool calling, orchestration, RAG, memory, API normalization) and then set up a thin vertical slice to validate latency, accuracy, and failure modes early. Which APIs are priorities for Phase 2 first (Amadeus/Skyscanner/Maps/Hotels/Payments), and do you prefer LangGraph, LangChain, or LlamaIndex for orchestration? If you share your current Phase 1 architecture, we can outline a concrete plan and timeline.

@letswork92

Your vision for Book With AI as an advanced travel intelligence platform is impressive, but realizing such a sophisticated system requires expert engineering and architecture to manage complex workflows and ensure scalability. With over 12 years of experience, I specialize in designing scalable AI systems using OpenAI APIs, RAG, and multi-agent architectures. My proficiency with technologies like Node.js for backend orchestration and React.js for front-end frameworks aligns well with your objectives. Additionally, I have substantial experience integrating various travel APIs such as Amadeus and Skyscanner, ensuring seamless data normalization and efficient API management. For Phase 2, I propose developing a robust conversational memory framework that supports intelligent recommendations while maintaining context through advanced prompt engineering. This will enhance user interactions significantly. My question for you is: How do you envision the user experience evolving with the new features in this phase?

@shadabkhan92

I’m MOHD SADAB, but you can call me your ticket to the fully-functioning, real AI world. Over the years, I have developed a keen interest in creating AI structures beyond the prototype phase. In fact, our core expertise centers around autonomous agents, LLM integrations and predictive ML models designed to slot seamlessly into your established undertakings, arming you with valuable insights aiding in noteworthy decision-making. Apart from demonstrating dexterity in executing advanced web and mobile stacks including React, Flutter, Django, and Node.js - we’ve taken things a step further. We're adept at deploying our solutions across notable platforms such as AWS, GCP and Azure. From fabricating tailor-made IoT hardware to designing game-changing PCBs; we’re no strangers to pushing boundaries and coming through with unparalleled outcomes on every front.

@ravikumarsingh1

I reviewed your Book With AI Phase 2 vision and this is exactly the type of AI platform where architecture, orchestration, memory, and travel-domain logic matter more than simply connecting an LLM. My experience includes: • Agentic AI systems and multi-agent workflows • OpenAI, Claude, LangChain, LangGraph, and RAG architectures • Vector databases (Pinecone, Qdrant, Weaviate) • Memory and context-management systems • API orchestration and workflow automation • AWS cloud infrastructure and scalable backend design • Travel, CRM, and recommendation-engine integrations My proposed approach: • Multi-agent architecture (Flight Agent, Pricing Agent, Visa Agent, Trip Manager, Recommendation Agent) • Hybrid memory layer (session memory + user profile memory + itinerary memory) • RAG-powered travel knowledge system • Travel Logic Engine for fare rules, baggage policies, layover validation, and airline recommendations • API orchestration layer with retry, fallback, caching, and cost optimization • Event-driven cloud architecture designed for future high-volume traffic Deliverables: ✔ AI orchestration architecture ✔ Conversational memory framework ✔ Travel intelligence engine ✔ Vector database implementation ✔ API orchestration framework ✔ Monitoring and analytics design I am interested in discussing Phase 2 as a long-term technical partnership and would be happy to review the current Phase 1 architecture before proposing the final stack, timeline, and implementation roadmap.

@GrowthBotz

- Hi, I'm GrowthBotz. I'd be happy to help with Senior AI Architect / AI Engineering Team Required – Book With AI (Phase 2). - I understand this needs AI automation, web builds, and business systems, and I would treat it as a strategic build scope. - We can start right away with the current files, site, workflow, or access details. - Our goal is to complete the work quickly, cleanly, and with strong attention to detail. - We aim to finish faster than other applicants while still keeping the final result accurate and professional. - We will align on the look early and build the finished version around a clean, professional direction. - We are reliable, responsive, and easy to work with; communication and payment stay inside Freelancer. - We're flexible on pricing for the right project, especially for clients looking for strong results and honest feedback. - We can schedule a quick call immediately if you'd like to review scope, timing, or priorities. - Looking forward to working with you; we're ready to get started whenever you are.

@thalessantana

I can design and build the Phase 2 AI travel intelligence layer for Book With AI, including: • Agentic AI system (LangGraph/LangChain or similar) for multi-step travel reasoning • Conversational memory system (short + long-term user context with vector DB like Pinecone/Qdrant) • RAG pipeline for travel knowledge + airline rules + policies • Flight intelligence layer (multi-city logic, layover validation, fare constraints, date optimization) • API orchestration layer (Amadeus/Skyscanner/Google Maps) with retries, caching, rate-limit handling • Travel recommendation engine (alternatives, cheaper routes, airport swaps) • Scalable backend architecture for 300k+ daily users (AWS/GCP, queue-based processing, microservices) Tech stack: Python (FastAPI), LangGraph/LangChain, PostgreSQL, Redis, Qdrant/Pinecone, Kafka/SQS, Docker/Kubernetes, OpenAI APIs. Deliverables: • AI system architecture + diagrams • Working orchestration layer for travel agents • Memory + RAG implementation • API integration framework • Deployment setup (cloud-ready) • Monitoring + logging system Timeline: 4–8 weeks depending on existing Phase 1 maturity