Enterprise AI Multi-Agent Infrastructure & Tiered Graph Architect

@irsolutions

With over a decade of experience in high-scale systems and enterprise architecture, I understand your need for a Technical Lead to spearhead the design and implementation of your complex AI infrastructure project for ZYRIX. My background in scaling systems for over 1 million users and developing high-security FinTech solutions directly applies to the challenges of creating a scalable, robust, and cost-efficient enterprise-grade system like yours. For your Tiered Graph Architecture, I recommend implementing intelligent routing logic that dynamically switches between 'Small' and 'Frontier' models based on task classification to optimize speed and cost. Drawing on my experience in building systems with similar complexities, such as the Telegram Mini Apps, where efficient routing and resource allocation were crucial, I am confident in my ability to design and implement this architecture effectively. I invite you to reach out to discuss how I can contribute to the success of your project and work together as long-term technical partners for ZYRIX on a profit-sharing basis. Let's connect and discuss the roadmap to achieving your goals.

@vishal1145

Your current architecture will collapse under token costs if you're routing every query through frontier models. Without intelligent task classification at the entry point, you'll burn through your budget before hitting 1000 users. The bigger risk is your persistent memory layer - if you're storing raw agent outputs without compression or semantic indexing, PostgreSQL will become a bottleneck at scale. Before I map out the routing logic, I need clarity on two things: What's your current token-to-database write ratio during a typical multi-agent execution? And are you planning to implement vector embeddings for memory retrieval, or are you relying on relational queries to reconstruct agent context? Here's the architectural approach: - NODE.JS + POSTGRESQL: Build a state machine that persists agent decisions as versioned JSON with JSONB indexing, allowing sub-50ms context retrieval across 10+ concurrent agent threads without table locks. - LLM PROMPT ENGINEERING + MODEL ROUTING: Implement a classification layer using a fine-tuned small model (GPT-3.5 or Claude Haiku) that scores task complexity in under 200ms, routing 70% of queries to cost-efficient models and reserving frontier models for multi-step reasoning tasks. - TOKEN TELEMETRY: Stream token counts to a separate analytics table with triggers that calculate cost per execution path in real-time, exposing this data via WebSocket for dashboard monitoring. - PERSISTENT MULTI-AGENT MEMORY: Design a shared knowledge graph where each agent writes findings to a central "memory pool" table with foreign keys linking to task context, preventing duplicate reasoning and reducing redundant API calls by 40%. I've built three production multi-agent systems that handle 50K+ daily executions. I'll send over a technical flowchart showing my last implementation - a legal document analysis platform where 5 specialized agents collaborated using a similar tiered routing system. We reduced token costs by 60% while maintaining 95% accuracy on complex queries. Let's schedule a 20-minute technical deep-dive before discussing the partnership structure. I don't commit to profit-sharing agreements without understanding your current infrastructure constraints and growth projections.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@alizain721

You need reliable model switching plus a single, persistent memory across agents — not just session blobs. That’s the real system risk: uncontrolled frontier calls and fragmented context. The trick is treating routing as cost-aware policy logic and memory as a shared, queryable knowledge layer so agents can reason incrementally without repeating expensive work. I built a multi-agent analytics assistant for a fintech client that used Postgres + pgvector for persistent memory and a routing layer that fell back from a small model to GPT-4 only when necessary. My approach: implement a lightweight classifier/router (small LLM + rules) that picks Small vs Frontier per task, an Agent Orchestrator that reads/writes global state in Postgres (pgvector for embeddings, normalized memory tables, advisory locks for concurrency), and a Token Telemetry middleware that logs per-execution tokens to Postgres for live cost dashboards. Deploy API on Vercel, background workers for long-running Frontier tasks, Stripe for billing events, SMTP for notifications. High-level architecture (text): Client -> Vercel API -> Router/Classifier -> [Small Model Pool] OR [Frontier Model Pool] |-> Agent Orchestrator <-> Postgres (memory tables + pgvector + locks) |-> Token Telemetry -> Postgres -> Cost Dashboard |-> Stripe / SMTP integrations Quick question: do you already have pgvector enabled on your Postgres instance, or should I include that setup in scope? My bid: $2250.

@mayankinnovative

Hi, As per my understanding: You need a Technical Lead to architect an enterprise grade AI system with tiered model routing, persistent multi agent memory stored in Postgres, and real time token telemetry with cost tracking. The system runs on Vercel with Node.js, integrates Stripe and SMTP, and already has a large schema and API surface. The goal is scalable, cost efficient orchestration with intelligent switching between small and frontier models based on task classification, plus durable shared agent state. Implementation approach: I will design a tiered graph orchestration layer where a classifier routes tasks to lightweight or frontier models using defined complexity thresholds and cost policies. A central memory service will persist agent state, embeddings, task artifacts, and cross agent references in Postgres using structured schemas and vector indexing. Agents will interact through an event driven workflow, with execution logs and token telemetry captured per route and stored for analytics and billing integration with Stripe. I will provide a high level architecture diagram outlining routing logic, memory persistence layer, telemetry pipeline, and API boundaries before development. A few quick questions: Are you using vector extensions in Postgres? Do you already have a task classification strategy? What are your current monthly token volumes?

@durgesh8527

With over a decade of experience in full stack development, I am confident that I possess the skill set required to tackle your complex AI infrastructure project head-on. Having built several multi-agent systems, I excel in crafting architectures that maximize efficient processing and intelligent decision making. To showcase my proficiency, I have provided detailed technical flowcharts and architecture diagram of a similar system that I have developed. During my career, I prioritize scalability, robustness, and cost-efficiency in all the projects I undertake; attributes you rightfully consider important for your enterprise-grade system. My deep understanding of Node.js and PostgreSQL— both of which are integral to your tech stack—combined with my ability to leverage tools like Vercel (Node.js), Stripe, and SMTP effectively provide further assurance of my competency. In conclusion, I offer not just the technical expertise but a collaborative spirit ready to work seamlessly with your organization in a profit-sharing model. Look no further for an experienced technical partner who can deliver clean code efficiently within set timelines without compromising on quality or communication.

@chraheelbhatti

With over a decade of hands-on experience as an AI professional, I can confidently say that the project you described is right up my alley. Having spent years designing, developing, and deploying advanced AI systems for organizations like Unilever Pakistan and State Bank of Pakistan, I understand the immense responsibility and complexity that come with building large-scale, enterprise-grade solutions like the one you require. My expertise in areas such as Computer Vision, NLP, Generative AI, and Cloud-based AI solutions would be valuable in devising intelligent routing logic to switch between 'Small' and 'Frontier' models based on task classification. Finally, as someone who has spearheaded both academic research projects and real-world AI implementations for major institutions, I believe I can bring a unique perspective to your vision. Not only do I offer a strong technical foundation with my PhD's degree in AI & Machine Learning and broad-ranging technical skills but also an understanding of what it takes to deliver high-quality research services that align with business objectives. Let's collaborate for long-term growth and profitable outcomes!

@KarthikTechCon

Hi, I’m Karthik with 15+ years of experience building enterprise AI platforms, multi-agent systems, and scalable backend architectures. I can help design and implement your ZYRIX infrastructure with: * Tiered model routing between fast/cost-efficient small models and frontier models for complex reasoning * Persistent shared memory using PostgreSQL to store agent state, findings, history, embeddings, and execution traces * Multi-agent workflows with planner, researcher, verifier, and summarizer agents * Real-time token usage and cost telemetry per workflow and model route * Backend on Node.js with deployment via Vercel * Integration with Stripe and SMTP notifications I understand the importance of persistent memory vs session-based memory and can build resumable workflows, audit trails, and graph-based execution logic. Example flow: Request → Task Classification → Model Router → Agent Chain → Persistent Memory → Final Output → Cost Tracking I can also provide a high-level architecture diagram and flowchart during the discussion. Warm Regards, Karthik B Resonite Tech

@AZTURK1

Hi there, I understand your Vercel (Node.js) + PostgreSQL stack and requirement for a Tiered Graph Architecture that routes between Small and Frontier LLMs, persistent multi-agent memory in Postgres, and token-level telemetry; I’ve designed similar enterprise multi-agent systems with persisted global state and routing policies. - Deliverable 1: Design & implement Tiered Graph routing module (task classifier, dynamic cost/latency policy, model switcher) with Node.js on Vercel and sample routing policies. - Deliverable 2: Persistent Multi-Agent Memory service using PostgreSQL schema migrations for global state, agent journals, versioning, and ACID-safe commits; integrated agent pub/sub and transactional reads. - Deliverable 3: Token Telemetry pipeline (per-execution tracing, cost calculator, Stripe-linked billing hooks, and SMTP alerts) plus an admin endpoint for usage/cost breakdowns. - Risk/QA: backup checkpoint + staged deployment with post-deploy validation and rollback plan to ensure zero-data-loss. Skills: ✅ Vercel (Node.js) ✅ PostgreSQL ✅ LLM Prompt Engineering ✅ Tiered Graph Architecture / model routing ✅ Token telemetry / cost monitoring ✅ Stripe & SMTP integration Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I’m available to start immediately; Is this currently deployed in a live production environment on Vercel, or should I prepare a staged rollout plan for your existing 35+ tables and 60+ endpoints? Best regards,

@nicolasalexandro

I can act as your Technical Lead to design and implement an enterprise-grade multi-agent AI infrastructure with a robust tiered graph architecture. Your focus on complex orchestration, scalability, and reliability aligns closely with the systems I’ve led for large-scale AI and data platforms. I’ve designed distributed agent frameworks, knowledge graphs, and vector-based retrieval layers for enterprise clients, ensuring they integrate with existing data systems, comply with security requirements, and remain observable and debuggable in production. This includes setting standards, patterns, and guardrails for other engineers to build on. My approach would begin with a clear architecture blueprint, selection of core components, and definition of interaction protocols between agents and graph tiers. From there, we’d move into a phased implementation with testing, monitoring, and documentation built in. I would love to chat more about your project! Regards

@pinkeegupta

Hi, I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju

@aldybaevstanisl5

Hi, You’re building more than an AI feature set here, this is a full multi-agent infrastructure with persistent memory and intelligent model routing, and the real challenge is making all of that work reliably under real usage, not just in isolated flows. The critical part is designing a system where agents can collaborate through a shared, persistent state while routing tasks dynamically between smaller and frontier models based on complexity and cost. That requires a clean orchestration layer, structured memory design in PostgreSQL, and a routing mechanism that is both predictable and efficient. The way I’d approach this is by first defining the architecture for agent communication and memory persistence, then implementing a routing layer that classifies tasks and selects models accordingly, and finally integrating token telemetry so every execution path is measurable and optimizable. I’ve worked on backend systems where orchestration, state management, and scalability are key, so the focus would be on building something stable, observable, and cost-aware from the start. One question: do you already have a defined strategy for task classification between small and frontier models, or should that be designed as part of the architecture? If this aligns with what you're looking for, we can discuss further in chat. Best Stanislav

@Visharaa2024

Hello, I’m very interested in leading the design and implementation of your enterprise grade AI infrastructure for ZYRIX. With 8+ years of experience in backend systems, distributed architectures, and LLM based applications, I have built scalable multi agent platforms focused on performance, cost efficiency, and reliability. My approach aligns closely with your requirements, particularly around tiered model routing, persistent memory, and token telemetry. I would design a routing layer using Node.js that intelligently classifies tasks and dynamically routes them between lightweight models for speed and cost efficiency and frontier models for complex reasoning. Persistent multi agent memory will be implemented using PostgreSQL, where all agents share and update structured state, enabling true collaboration beyond session limits. For token telemetry, I will implement real time tracking of usage, latency, and cost per task, ensuring full visibility and optimization. The system will integrate seamlessly with Stripe for billing and SMTP for notifications while maintaining scalability across your existing infrastructure. I can provide a clear high level architecture diagram demonstrating model routing, shared memory flow, and agent coordination. I’m ready to contribute as a long-term technical partner and help scale ZYRIX into a robust, efficient AI platform.

@mugencodes

Hi, hope you are well. I have hands-on experience implementing solutions with Node.js, PostgreSQL, Backend Development, particularly in building scalable and well-structured systems. I understand your goals and will deliver clear work on time while keeping you updated. As a project manager with many years of experience in Node.js, PostgreSQL, Backend Development, I helped many clients reach their goals. Visit our website and check our work style and team members Looking forward to working with you, connect in chat or talk on a call. Regards, Jayabrata Bhaduri

@rishi9343

I will design and implement a scalable AI infrastructure with a tiered graph architecture, utilizing intelligent routing logic to switch between 'Small' and 'Frontier' models based on task classification, and implement persistent multi-agent memory using PostgreSQL, enabling real-time token tracking and cost monitoring, to deliver a robust and cost-efficient enterprise-grade system with a high-level technical flowchart.

@Ashnasajid

Hi, I can do this. With extensive experience in designing scalable AI infrastructures, I am well-equipped to implement the complex system you require. My expertise in Node.js, PostgreSQL, and AI aligns perfectly with your project needs. I will develop a tiered graph architecture that intelligently routes tasks between 'Small' and 'Frontier' models, ensuring optimal performance and cost efficiency. The persistent multi-agent memory will enable seamless collaboration among AI agents, with all data stored in a PostgreSQL database for durability. Additionally, I will implement real-time token telemetry for effective cost monitoring. I have previously built similar multi-agent systems and can provide a high-level technical flowchart upon request. I understand the intricacies of persistent memory and model routing, ensuring a robust solution. I look forward to the opportunity to partner with ZYRIX on this exciting project. Ashnasajid

@guptakishan805

Hello, I can design a scalable multi-agent AI system with intelligent model routing and persistent memory. Architecture Flow (High-Level): User Request → Task Classifier → Router (Small vs Frontier Model) → Agent Orchestrator → Shared Memory (PostgreSQL) → Response • Model Routing: lightweight classifier (rules + embedding similarity) to choose cost-efficient vs high-reasoning models • Persistent Memory: agents write/read structured state (tasks, embeddings, logs) from PostgreSQL (not session-based) • Multi-Agent System: role-based agents (planner, executor, validator) collaborating via shared state • Token Telemetry: middleware tracking tokens, cost per route, stored for analytics Built with Node.js (Vercel), Postgres, and modular APIs for 60+ endpoints. I can lead this end-to-end with clean architecture and long-term scalability.

@adily1

Hey , I just finished reading the job description and I see you are looking for someone experienced in Node.js, LLM Prompt Engineering, PostgreSQL, Backend Development and Artificial Intelligence. This is something I can do. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: 1. These are all the requirements? If not, Please share more detailed requirements. 2. Do you currently have anything done for the job or it has to be done from scratch? 3. What is the timeline to get this done? Why Choose Me? 1. I have done more than 250 major projects. 2. I have not received a single bad feedback since the last 5-6 years. 3. You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. Timings: 9am - 9pm Eastern Time (I work as a full time freelancer) I will share with you my recent work in the private chat due to privacy concerns! Please start the chat to discuss it further. Regards, Adil.

@davidt143

⭕ AI Infrastructure Technical Lead ⭕ I saw your posting for a Technical Lead for AI infrastructure and am confident I’m the perfect fit. I have been freelancing full-time for 5 years and have capacity to take on an additional client at this time for 40 hours a week. -------- I’m the best candidate, Why? -------- I’ve designed multi-agent AI systems with dynamic model routing and persistent memory layers. I understand how to balance cost vs performance using task classification, routing simple queries to lightweight models and escalating complex reasoning to frontier models. I’ve also implemented token telemetry pipelines for real-time cost tracking. -------- What am I good at? -------- My zone of genius is system architecture for AI at scale. I can design a tiered graph with routing controllers, agent orchestration, and PostgreSQL-backed shared memory (entity store + vector context + execution logs). Persistent memory is handled via structured tables + embeddings for recall. Model routing is driven by classifier + heuristics + feedback loop. I’ll also integrate Stripe billing hooks and monitoring across your Node/Vercel stack. -------- I am interested, Why? -------- ZYRIX’s vision for a scalable, cost-efficient AI system aligns with how I build—modular, observable, and production-ready. I’m interested in long-term ownership and optimizing both performance and profit. Looking forward our collaboration Cheers.