AI Agent Developer – OpenAI / Claude & LLM Applications

@MashoodurRehman1

I am an experienced AI Agent Developer with a strong background in leveraging Large Language Models (LLMs) for building and deploying advanced AI applications. I have extensive experience integrating OpenAI and Claude APIs, creating scalable AI solutions tailored for enterprise needs, and developing Python-based applications using frameworks like FastAPI. My expertise includes building Retrieval-Augmented Generation (RAG) pipelines and orchestrating multi-agent systems using LangChain and LangGraph. I am proficient in developing complex AI architectures and optimizing workflows to enhance operational efficiency. With skills in vector databases, memory systems, and tool calling, I am adept at turning AI concepts into practical, production-ready solutions that seamlessly integrate with business systems. I am interested in learning more about your specific use cases and discussing how my experience aligns with your project needs. Please feel free to reach out for more details on my past projects and how I can contribute to your AI initiatives.

@owebest

Hi, Enterprise AI development requires more than connecting an LLM API. The key challenge is building reliable AI systems that understand context, use business data accurately, and perform consistently in real production environments. Our approach includes: • Developing AI agent workflows with tool calling and external API integrations. • Building RAG pipelines focused on accurate knowledge retrieval and reduced hallucination. • Creating scalable backend systems for AI applications and enterprise workflows. • Designing maintainable AI architectures that can evolve with business needs. A common challenge in AI projects is that a vector database alone does not guarantee accurate results. Document quality, retrieval logic, context handling, and evaluation processes play a major role in building a trusted AI solution. We have experience developing AI powered applications and can help turn your AI concepts into production ready systems with the right architecture. I would be glad to discuss your requirements and the best approach for your AI platform. Thank you. Best Regards, Team Owebest || KD

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Intelligent AI Applications with Expert AI Agent Development ❇️ Hi My Friend, I hope you're doing well. I've gone through your project requirements and see you are looking for experienced AI Agent Developers. You don't need to look any further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects focused on AI applications. I will create scalable AI solutions using modern LLM technologies, ensuring your needs are met within your budget. ➡️ Why Me? I can easily build your AI-powered applications as I have 5 years of experience in AI development, specializing in LLM integration, prompt workflows, and scalable solutions. My expertise includes working with OpenAI and Claude APIs, as well as developing Python applications using FastAPI. Additionally, I have a strong grip on LangChain, vector databases, and multi-agent orchestration. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I'm looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ AI Application Development ✅ LLM Integration ✅ Python Programming ✅ FastAPI Framework ✅ Prompt Workflow Design ✅ API Integration ✅ RAG Pipeline Creation ✅ LangChain & LangGraph ✅ Vector Databases ✅ Multi-Agent Orchestration ✅ Memory Systems ✅ AI Governance Practices Waiting for your response! Best Regards, Zohaib

@diektech

Hi — Elias here from Miami. I see you're looking to develop intelligent AI-powered applications. The goal is to create robust solutions that leverage advanced language models effectively. What usually matters most here is the integration of AI with existing systems while ensuring scalability and maintainability. A common issue in projects like this is managing the complexity of AI workflows and the interactions between multiple components. The tricky part is usually ensuring that the AI models can be effectively trained and deployed without creating bottlenecks. My practical approach would involve designing a modular architecture that allows for easy updates and expansions. This ensures both stability and flexibility as your needs evolve. I have experience with LLM implementations and have successfully integrated AI agents into various platforms. A few questions to better understand the scope: Q1 – What specific user roles and permissions are you envisioning for the application? Q2 – Are there any existing systems or APIs that this AI solution needs to integrate with? Q3 – What are your expectations regarding the application’s scalability and performance metrics? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@corpmember29

Hello, I HAVE SUCCESSFULLY BUILT SIMILAR AI AGENTS, LLM-POWERED APPLICATIONS, RAG SYSTEMS, AND ENTERPRISE AI AUTOMATION PLATFORMS AND CAN SHOW YOU MY PREVIOUS WORK. I have carefully reviewed your requirements and understand that you are looking for an AI Agent Developer to build production-ready AI applications using OpenAI, Claude, RAG pipelines, FastAPI, LangChain/LangGraph, MCP, vector databases, tool calling, memory systems, and multi-agent orchestration. AI Agent Development Platform User Portal Authentication, User Profile, AI Agent Management, Chat Interface, Prompt Management, Knowledge Base, Document Upload, AI Workflows, Conversation History, Notifications, Settings ETC Admin Panel Dashboard, User Management, AI Agent Management, Prompt & Workflow Management, Knowledge Base Management, LLM Configuration, API Management, Usage Monitoring, Reports & Analytics, Roles & Permissions, System Logs, Settings & Configuration ETC AI Automation Engine AI Agent Creation, OpenAI/Claude Integration, Prompt Engineering, RAG Pipeline, Vector Database Integration, Tool Calling, Memory Management, Multi-Agent Workflow, API Integration, Background Processing, Monitoring & Error Handling ETC. I am available on desk as per your convenient time zone and will work on your project until you satisfied with my work. Thanks Christina

@efanntyo

Hi there, I am excited to submit my proposal for your AI Agent Developer project. With extensive experience in developing and deploying AI-powered applications, I am well-equipped to meet your project requirements. My expertise lies in leveraging modern LLM technologies, including OpenAI and Claude APIs, to create intelligent and scalable AI solutions tailored for enterprise use cases. I have a proven track record of building AI agents, designing prompt workflows, and integrating external tools and APIs to enhance application capabilities. My experience with Retrieval-Augmented Generation (RAG) pipelines and Python-based frameworks like FastAPI ensures the development of efficient and robust AI applications. Furthermore, I am adept at using advanced AI systems such as LangChain, LangGraph, and vector databases to optimize AI workflows and enhance multi-agent orchestration. My approach involves architecting enterprise AI platforms with a focus on integrating complex business systems, establishing AI governance, and optimizing workflows for maximum efficiency. I am committed to delivering production-ready solutions that align with your business objectives. I am eager to bring my skills and experience to your project and contribute to the successful deployment of your AI applications. Thank you for considering my proposal. Best Regards,

@InterconnectBPO

I can help you build and deploy intelligent AI-powered applications using modern Large Language Model technologies. My experience with AI agents and scalable solutions will ensure we meet your enterprise needs effectively. I noticed your emphasis on integrating LLM APIs and developing prompt workflows. These are critical for creating robust AI applications that can adapt to complex business systems. I specialize in advanced AI systems, including LangChain and vector databases, and have successfully architected enterprise AI platforms. We have 20+ 5-star reviews on similar projects! Regards, InterconnectBPO

@nikhilgarg96

Hi, I'm a Full Stack AI Developer with experience building production-ready AI applications powered by **OpenAI, Claude, LangChain, LangGraph, FastAPI, and vector databases**. I specialize in developing AI agents that automate business workflows, integrate with external APIs, and deliver reliable, scalable solutions. My expertise includes: * Building AI agents using OpenAI and Claude APIs * Developing RAG pipelines with Pinecone, Weaviate, Milvus, and PostgreSQL (pgvector) * LangChain and LangGraph for agent orchestration and complex workflows * FastAPI and Python for secure, high-performance AI backends * Tool calling, memory systems, MCP integration, and multi-agent architectures * REST API development and integration with CRM, ERP, and third-party business systems * Prompt engineering, AI workflow optimization, and production deployment * Docker, Kubernetes, AWS, and CI/CD for scalable cloud deployments I focus on building AI solutions that are secure, maintainable, and production-ready, whether it's intelligent assistants, document processing, knowledge retrieval systems, workflow automation, or enterprise AI platforms. I'm available to start immediately and would be happy to discuss how I can help design and deliver a scalable AI solution. Best regards, NIkhil...

@C0RETECHIES

Hi there, I see you're looking to build operational AI agents capable of executing complex, multi-step enterprise workflows. This involves orchestrating LLMs like OpenAI and Claude with external tools, APIs, and private knowledge bases via RAG to create autonomous systems that can be embedded into core business processes. Technical approach: We'll use a FastAPI backend. LangGraph is ideal for orchestrating multi-agent workflows and managing complex state. For knowledge retrieval, we'll build a RAG pipeline with a vector database. Agents will be equipped with a well-defined set of tools for API calls and data manipulation. Core modules: An Orchestration Engine to manage agent communication and task handoffs. A Tool & API Integration Layer for reliable external interactions. A RAG module for document ingestion, embedding, and retrieval. A persistent Memory System for contextual continuity. Relevant systems: We recently developed an 8-Agent AI Pipeline for lead generation, which orchestrates discovery, enrichment, and outreach agents. We also built a Slack assistant using LangChain and a dual OpenAI/Claude model architecture for complex reasoning tasks. We would start with a single-agent proof-of-concept to validate a key workflow and its integrations, then scale to a multi-agent system with a focus on robust logging and performance evaluation. Regards, Rohit

@ShaikhAneesa

Hi there, I understand you are looking for an experienced AI Agent developer to build production-ready AI applications using modern LLM technologies, integrating OpenAI and Claude APIs with scalable architectures, RAG pipelines, external tools, and enterprise workflows. I am confident I can deliver secure, scalable, and maintainable AI solutions tailored to your business needs. My approach will be to build a modular Python/FastAPI application using OpenAI or Claude APIs with LangChain or LangGraph for agent orchestration, memory, tool calling, and multi-step reasoning. I'll implement a RAG pipeline with a vector database for accurate retrieval, integrate external APIs and business systems, and create reusable prompt workflows with structured outputs, robust error handling, logging, and monitoring. The solution will be designed for scalability, governance, and future expansion into multi-agent workflows while keeping the architecture clean and easy to maintain. The deliverable will include a production-ready AI agent, RAG implementation, API integrations, documented prompt workflows, deployment guidance, and clean, well-commented source code. Could you clarify whether the primary use case is customer support, internal automation, document intelligence, or another enterprise workflow? I'm ready to start immediately. Warm Regards, Aneesa.

@opencollar

Hi, I understand that making AI agents that are smart and reliable is a big task. I saw your need for smooth integration of LLM APIs and building useful prompt systems, which are key to creating good AI solutions. I will handle other parts like connecting tools and API management easily. Out of curiosity, do you want these AI agents to also learn from new data on the go or just stick to predefined info? Let's chat about how we can plan and create something truly impactful together. Regards, Nick. I’ve worked on platforms like the Car fleet management system, the hotel API, and the freelancer marketplace, which shows I can handle complex and varied projects.

@ahmadayaz

With expertise in AI technologies and a background in developing intelligent applications, I propose a comprehensive approach to building AI solutions for your enterprise. I specialize in integrating Large Language Models (LLMs) and AI agents, creating Retrieval-Augmented Generation (RAG) pipelines, and architecting enterprise AI platforms. I have experience with advanced AI systems like LangChain and multi-agent orchestration. To ensure project success, I would inquire about specific performance metrics, existing data infrastructure, compliance requirements, and project timelines. Let's collaborate to deliver cutting-edge AI solutions tailored to your business needs and ensure a lasting partnership for future projects.

@techplusintl

Hi, To build and deploy intelligent AI-powered applications, I can create scalable AI solutions using modern LLM technologies. I have experience developing AI agents and integrating LLM APIs. I have worked on projects using OpenAI and Claude APIs, and I understand how to create prompt workflows and RAG pipelines. My plan is to first understand your specific requirements and then design the architecture for the AI applications. I will ensure that the solutions are production-ready and can integrate with your existing systems. Do you have any specific tools or frameworks you prefer for this project? I invite you to chat with me to discuss this project further.

@TeamGTW

Hello! Based on your project description, you are looking to build production ready AI powered applications using OpenAI, Claude, and modern LLM technologies. The solution will involve developing intelligent AI agents, integrating LLM APIs, creating RAG pipelines, implementing prompt workflows, connecting external tools and business systems, and delivering scalable Python applications with FastAPI. The platform should support enterprise grade features including memory, tool calling, vector databases, multi agent orchestration, and AI governance. I will focus on building secure, scalable, and maintainable AI solutions using Python, FastAPI, LangChain, LangGraph, MCP, vector databases, and modern LLM APIs. I will design efficient RAG pipelines, optimize prompt workflows, integrate third party APIs and business systems, and ensure the architecture is ready for future expansion while maintaining performance, reliability, and clean documentation. I specialize in AI application development with 7+ years experience and I have done similar work in past please open the chat window so I can share with you. Thank you for considering my proposal. I look forward to hearing from you soon. Best regards, Nikita Gupta. 1. What is the primary business use case you want the AI agents to solve in the first phase? 2. Is this a long term engagement where new AI features and workflows will be added over time?

@AhteshamAK

Hi there, I like the focus here: production-ready AI agents, not just demos. I can help shape the agent architecture around OpenAI, Claude-style integrations, RAG, tool calling, memory, and Python/FastAPI services so the system is reliable enough for enterprise workflows. For the Large Language Models (LLMs) side, I’d focus on clean orchestration, prompt workflows, vector search, API integrations, evaluation loops, and guardrails so the agents behave consistently in real business cases. Strong LLM Prompt Engineering will be built into the flow, not treated as an afterthought. Which first agent should we bring to life: a research helper, workflow automator, support brain, or something even more fun? Do you already have preferred vector databases or cloud infrastructure in place? Are these agents meant to support internal teams, customer-facing users, or both? Best, Ahtesham Ahmed

@juanpablomacf

Building agents that actually think through tools and external APIs is where I spend most of my time, so this one caught my attention immediately. I’ve built LLM agents with OpenAI, Claude, LangGraph and custom FastAPI backends, plus RAG pipelines with vector DBs and multi‑agent orchestration for real business workflows. I’d start by mapping the core tools your agent needs to call, then shape the prompt and memory flow so the system behaves consistently before scaling it into a full platform. Which LLM do you prefer as the primary model? Do you already have a vector database in mind? Should the agent run single‑agent or multi‑agent workflows? Juan Pablo

@ImranAli01

Hi there, I got that you are looking for an experienced AI Agent Developer to build and deploy intelligent AI-powered applications using modern Large Language Model (LLM) technologies, integrating LLM APIs, and creating scalable AI solutions for enterprise use cases. This is what I can help you with, let's chat. My approach is to leverage advanced AI systems using LangChain, LangGraph, MCP, vector databases, tool calling, memory systems, and multi-agent orchestration to architect enterprise AI platforms, optimize workflows, establish governance practices, and integrate AI agents with complex business systems. By developing prompt workflows, integrating external tools and APIs, and creating Retrieval-Augmented Generation (RAG) pipelines, I ensure the delivery of efficient and effective Python-based applications using frameworks like FastAPI. As final deliverables, you will receive fully functional AI applications integrated with OpenAI and Claude APIs, designed to meet your enterprise AI needs seamlessly. One thing I'd like to confirm before we start: Are there any specific business systems or external APIs you require integration with? Looking forward to discussing how we can bring your AI vision to life. Regards, Imran

@Hubready

Hello!, I am a Florida-based senior software engineer(frontend, backend, ecommerce, etc) with about 15 years of experience building production-grade AI and software systems. I read your AI Agent Developer post carefully, and the goal is clear: build intelligent LLM-powered applications that are reliable, useful, and ready to deploy, not just demos. I’ve worked extensively with Python, Java, OpenAI, Claude, prompt engineering, AI automation, and agent-style workflows, including multi-step reasoning, tool use, workflow orchestration, and API integrations. My approach is practical: define the agent behavior first, map the tools, prompts, memory, and safety rules, then test edge cases before deployment. A few relevant examples from my recent work: - internal support copilot for a SaaS team - document Q&A assistant with retrieval and citations - automation bot for lead qualification and routing - research assistant that chains tasks across APIs Could you please clarify the following questions to help me better understand the project? 1. What should the AI agent do day to day, chat assistant, task automation, research, or multi-agent workflows? 2. Do you already have a preferred stack or deployment target, such as Python backend, Java services, or a specific cloud? 3. Should the first phase focus on a working MVP, or do you want the architecture designed for scaling from day one? If you want someone who pays attention to the details, asks the right questions, and can move fast

@vishal1145

Your RAG pipeline will fail in production if your vector embeddings drift as your knowledge base scales beyond 100K documents. This causes hallucinations and incorrect retrieval that erodes user trust. Quick questions - are you planning semantic chunking or fixed-size splits for document ingestion? And what's your target response latency for multi-agent orchestration under concurrent load? Here is the architectural approach: - OPENAI/CLAUDE INTEGRATION: Build fault-tolerant API wrapper with exponential backoff, token usage monitoring, and fallback routing between providers to prevent downtime during rate limit spikes. - LANGCHAIN/LANGGRAPH: Design stateful multi-agent workflows with memory persistence using Redis, enabling context retention across sessions and parallel tool execution without race conditions. - RAG PIPELINE: Implement hybrid search combining dense embeddings (Pinecone/Weaviate) with BM25 sparse retrieval, plus reranking layer to boost precision from 60% to 92% on domain-specific queries. I've architected similar LLM systems for 2 enterprise clients processing 500K+ agent interactions monthly without hallucination issues. Let's schedule a 20-minute technical call to validate your vector database strategy before you scale.