LLM RAG Production Platform

@softsolution2000

I have extensive experience in JavaScript, Python, Software Architecture, React.js, and Full Stack Development, making me a perfect fit for the LLM RAG Production Platform project. I am confident that my skills align well with the requirements you have outlined. The budget can be adjusted once we discuss the full project scope, and I am committed to delivering within your budget. Please review my 15-year-old profile to see the quality of work I have consistently provided. Your satisfaction is my priority, and I am eager to start working on this project to demonstrate my commitment. Looking forward to discussing the job details with you.

@ZohaibRoy

⭐⭐⭐⭐⭐ Senior AI Engineer to Create Scalable AI Solutions for Business Insights ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and noticed you're looking for a Senior AI Engineer. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for AI systems. I will build AI pipelines that analyze conversations and create structured insights efficiently. ➡️ Why Me? I can easily handle your AI engineering needs as I have 5 years of experience in building AI systems, including Python and FastAPI. My skills cover LLM integration, RAG pipelines, and cloud deployment. I also have a strong grip on optimizing performance and costs while ensuring system reliability. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to chatting with you! ➡️ Skills & Experience: ✅ Python ✅ FastAPI ✅ LLM Integration ✅ RAG Pipelines ✅ Vector Search ✅ Cloud Deployment ✅ Docker ✅ System Reliability ✅ Performance Optimization ✅ Speech Analysis ✅ Cost Optimization ✅ Monitoring & Evaluation Waiting for your response! Best Regards, Zohaib

@koolvishruth

With 8+ years of experience in software development, Machine Learning, Automation, Integration and specializing in Python, ML, AI and more, I am the perfect candidate for your LLM RAG Production Platform project. My expertise include in-depth knowledge and practice with LLM APIs like OpenAI and others you have mentioned. I have substantial hands-on experience designing scalable backend architectures as well as familiar with Docker and cloud deployment which are integral for a successful production system. Additionally, raised on the Agile platform using JIRA and Trello is a testament to my ability to meet deadlines efficiently. Lets connect

@Julian29

Hello, I have reviewed your requirements carefully and understand the scope of the LLM RAG Production Platform. I bring over 10 years of professional experience in building scalable AI systems and production-grade software, and I can design and deliver the architecture and pipelines you need in a structured, stage-based approach. I will start by defining the system architecture, data flow, and pipeline stages, then move to building production-ready FastAPI microservices for speech-to-text, RAG, summarization, classification, objection detection, and recommendations. The platform will use scalable vector search, embeddings, reranking, and evaluation frameworks, with robust monitoring and cost-optimized LLM routing. I will implement strong hallucination control using retrieval verification, tool-augmented workflows, and evaluation benchmarks. I am available to work in your time zone and will continue until the solution meets your expectations. I eagerly await your positive response. Thanks.

@mahig47

Hello! As per your project post you are building a production grade LLM and RAG platform that turns real world business conversations into structured intelligence insights and automation. The goal is to move far beyond demos and ship scalable low latency AI systems that reliably process speech data apply advanced LLM reasoning and power downstream business decisions in a cost efficient and observable way. My focus will be on delivering end to end AI pipelines that are designed for real production usage. This includes conversation ingestion and structuring RAG pipelines with high quality retrieval and evaluation FastAPI based inference services and a system architecture that balances performance cost and reliability. The emphasis will be on systems that can be monitored tested improved and scaled with confidence. I specialize in taking AI products from concept to real production environments with a strong focus on system design maintainability and long term evolution. My focus will be on building a platform that your team can extend optimize and operate safely as usage and complexity grow. Let’s connect to align on product vision system ownership and a clear roadmap for building a production ready LLM RAG platform. Best regards, Nikita Gupta

@maximg10

⭐Hi, I’m ready to assist you right away!⭐ I believe I’d be a great fit for your LLM RAG Production Platform project since I have hands-on experience building and shipping scalable AI pipelines with real production LLM systems. I understand the importance of fast delivery while maintaining cost-efficient and low-latency workflows. I have strong expertise in Python, FastAPI microservices, and managing RAG pipelines including embeddings, vector search, and reranking. I have deployed production AI systems focusing on summarization, classification, and recommendation using OpenAI and similar LLM APIs. Your project’s main challenge is transforming unstructured conversation data into structured, actionable insights while ensuring reliability, scalability, and evaluation of the AI models. I can design and optimize the backend architecture to meet your business needs, reducing hallucinations and controlling inference costs effectively. If you have any questions, would like to discuss the project in more detail, or would like to know how I can help, we can schedule a meeting. Thank you. Maxim

@zherukha1

Hello, With an expansive 6+ years of experience as a full stack and Android developer, I bring to the table a set of skills and expertise that aligns directly with your LLM RAG Production Platform project. My proficiency in FastAPI, Python, React.js, and more positions me to effectively handle the task of building AI pipelines for the platform. Not only I am experienced in leveraging LLM APIs like OpenAI and Claude, but I also have a comprehensive understanding of RAG pipelines encompassing embeddings, vector DBs, retrieval optimization. In addition, I have in-depth experience in other areas key to your project including Docker, cloud deployment, system reliability along with design scalable backend architectures which were vital for building my previous projects. In addition to my technical capabilities, I possess the product mindset necessary to transform your business goals into a working AI system. Throughout my career, Product thinking has been at the forefront of my approach thus ensuring that the technology not only works but is also optimized for user performance. My extensive experience in creating SaaS platforms and healthcare apps has taught me about accountability, meeting deadlines, and producing high-quality work. Furthermore, I am not afraid of taking ownership or leading projects. Be it starting from scratch or optimizing existing systems by refactoring or modernization; I can do it all! As your Senior AI Engineer, Thanks!

@mughiraaa

hi, i have reviewed the details of your project. we have real production experience building llm powered systems that process conversations into structured insights and automation. i will execute this by designing a full ai pipeline starting from speech to text, then passing clean transcripts into llm workflows for summarization, classification, objection detection, and recommendations. i will build scalable fastapi services for inference, integrate rag using embeddings and vector search, and focus on low latency and cost control. evaluation, monitoring, and hallucination handling will be part of the core system from day one. Let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. i will share my portfolio in the chat. mughiraa

@kishanbwts

Hi, Your project to turn business conversations into actionable insights is exactly the kind of challenge I’ve tackled before. At a healthcare AI startup, I built production-grade LLM pipelines that processed recorded patient calls — converting speech to text, generating structured summaries, and flagging key concerns using FastAPI microservices. This included implementing RAG workflows with embeddings and vector search for better retrieval, plus monitoring and evaluation tools to handle hallucinations and optimize costs. A few questions to align on your needs: - Are you focused on specific conversation types or industries? - Do you prefer certain vector databases or cloud platforms for hosting? - What latency targets do you have for real-time inference? I can help design a scalable architecture that balances speed, cost, and accuracy, with Dockerized deployments for robust system reliability. Let’s jump in and start refining your AI pipeline for production readiness. Ready to get going when you are.

@vorasiddh4it

Hello Mark, I’m a software services provider with real production experience shipping LLM systems—not demos. I’ve built end-to-end speech → text → insights pipelines, RAG with embeddings/vector DBs, and low-latency FastAPI microservices running in Docker on cloud. I can show demo code + production patterns (evals, guardrails, cost controls) before we lock the deal. What I’ll Build for You (Production-Grade) 1) Conversation → Intelligence Pipeline • ASR (Whisper/Deepgram) → diarization → chunking • LLM summarization, classification, objection detection • Structured outputs (JSON schemas) + PII handling 2) RAG @ Scale • Embeddings (OpenAI/BGE), vector DB (FAISS/Pinecone) • Reranking, caching, eval harness (precision@k, win-rate) • Hallucination controls + prompt/versioning 3) FastAPI Microservices • Async inference, retries, rate limits • Cost/latency optimization (batching, caching) • Monitoring (traces, eval dashboards), CI/CD, Docker Tech Stack Python · FastAPI · OpenAI/Claude/Gemini · LangChain/LlamaIndex · FAISS/Pinecone · Docker · Cloud Relevant Projects CallSense AI – speech → insights for sales calls InsightRAG – embeddings + rerank + eval framework FastServe AI – low-latency LLM microservices If you want someone who ships, debugs hallucinations, and designs for failure paths first—I’m in. Share your current stack and data flow; I’ll propose a phased rollout and we’ll make the deal.

@KarthikTechCon

Hi, I’m Karthik, a Senior AI/ML & full-stack engineer with 10+ years of experience, including building production LLM systems and scalable AI backends. I focus on real-world AI that is reliable, measurable, and cost-efficient—not demo projects. Why I’m a strong fit • Strong Python + FastAPI for AI microservices • Hands-on with OpenAI/Claude/Gemini APIs in production • Built RAG pipelines (embeddings, vector DBs, reranking) • Experience deploying LLM systems with monitoring and evals • Focus on hallucination control and prompt/system design • Dockerized, cloud-ready architectures with CI/CD Relevant to your use case – Designed pipelines for speech → text → insights – LLM systems for summarization, classification, and recommendations – Cost/latency optimization via caching, batching, and model routing – Evaluation frameworks to track quality over time How I work – Architect-first mindset for scalable systems – Ship fast but with measurement and guardrails – Clear documentation and collaboration with product teams I’m interested in long-term ownership and scaling a serious AI product. Happy to discuss your current stack, scale targets, and roadmap. Best regards, Karthik

@phpxpert89

Hello Mark D., I checked your project, and it looks interesting. This is something we already work on, so the requirements are clear from the start. We mainly work on JavaScript, Python, Software Architecture, React.js, Full Stack Development, FastAPI, OpenAI, Large Language Model, AI Model Development, AI Development We focus on making things simple, reliable, and actually useful in real life not overcomplicated stuff. Let’s connect in chat and see if we’re a good fit for this. Best Regards, Ali nawaz

@Ussama91

I will design scalable AI pipelines, build LLM-based systems for conversation analysis, and develop FastAPI microservices for AI inference, leveraging my experience with Python, LLM APIs, and RAG pipelines to deliver production-ready systems with monitoring and performance optimization, adapting to the proposed budget. Waiting for your response in chat! Best Regards.

@khurshid040462

Hi there, I am excited about the opportunity to contribute to your groundbreaking AI product that transforms real-world conversations into actionable insights. With extensive experience in developing scalable AI pipelines and a solid background in deploying LLMs into production environments, I am confident in my ability to deliver high-performance solutions tailored to your needs. My expertise includes Python, FastAPI, and optimization of LLM workflows to handle cost and latency effectively. I can design and implement your required AI systems for summarization, classification, and RAG pipelines while ensuring robust monitoring and evaluation processes are in place. Let's discuss how I can contribute to your vision and timeline for this innovative project.

@NEHABHAT92

With over 9+ years as a skilled web and mobile app developer, my team and I offer a unique blend of technical expertise that I believe could be valuable for your AI production platform. Although we do not have direct experience with LLMs, we have built scalable systems with similar technologies and handled various challenges from evaluation frameworks to system reliability. Our forte lies in handling the entire life cycle from design to deployment in a cost-efficient manner ensuring low-latency and high performance—a skill that resonates perfectly with your project requirements! Combining our knowledge of FastAPI, Docker, Python, cloud deployment and more, there is no doubt that we can get your AI pipelines up and running to analyze conversations in no time. Furthermore, working on both E-commerce and CMS based websites has polished our eye for detail ensuring that all front-end/backend pipelines are not just functional, but also user-friendly. Beyond that, partnering with us gives you access to other added benefits such as effective project cost estimation, free after delivery support for three months and discounted domain/hosting options. Don't miss out on glimpsing your 'IDEA TURNED INTO REALITY'—let us make it happen. Let's embark on this long-term journey together!

@citijayamala

I have successfully developed AI agents for social media management (Meta/Facebook), customer support, lead generation, and appointment booking—all powered by n8n and integrated seamlessly with existing business systems. My expertise lies in designing end-to-end automation workflows that combine n8n orchestration with advanced AI models such as OpenAI GPT-4, Claude,Vapi, LLaMA, and other state-of-the-art LLMs, enabling intelligent, context-aware, and scalable business solutions. Sure, I can handle your project on developing an LLM RAG Production Platform with Python and FastAPI. kindly please connect in chat to discuss. I specialize in: • n8n Workflow Development: API integrations, webhook automation, multi-step workflows, and data transformations. • AI Agent Design: Conversational models, NLP/NLU pipelines, prompt engineering, and fine-tuning for domain-specific tasks. • Cross-Platform Integration: Social media APIs (Meta/Facebook, Instagram, LinkedIn), CRM systems, email marketing platforms, and custom backend systems. • Automation Infrastructure: Self-hosted n8n on Docker/VPS, cloud deployments, API authentication (OAuth, tokens), and data security best practices. • Advanced Use Cases: Intelligent lead qualification, AI-driven customer engagement, automated scheduling, and content generation pipelines. Whether it’s creating a fully automated sales funnel, AI-powered content research tool, or real-time customer support agent.

@Shrutibimpl

Hello, I will design and build a scalable, production-ready AI pipeline focused on transforming recorded business conversations into structured intelligence. I will implement a multi-stage system that handles speech-to-text conversion and then uses advanced LLM-based systems for summarization, classification, and objection detection. The solution will incorporate sophisticated RAG pipelines with embeddings, vector search, and reranking capabilities. The entire system will be deployed as cost-efficient, low-latency FastAPI microservices, complete with production-ready monitoring, evaluation metrics, and performance optimization. 1) Which specific cloud platform (AWS, GCP, Azure) are you currently using or planning to use for hosting the AI microservices? 2) What is the total approximate volume (in hours) of daily recorded conversation data that the pipeline needs to ingest? 3) Which LLM API or model (e.g., GPT, Claude, self-hosted) is the preferred choice for the summarization and classification tasks? Thanks, Bharat

@MIndlabsAi6

Hello Mark, I am Vishal Maharaj, with 20 years of expertise in JavaScript, Python, Software Architecture, React.js, Full Stack Development, FastAPI, OpenAI, and AI Development. I have carefully reviewed your requirements for the LLM RAG Production Platform project. I propose to design and implement scalable AI pipelines for analyzing conversations, utilizing LLM-based systems for summarization, classification, and objection detection. I will develop FastAPI microservices for AI inference, ensuring cost-efficiency and low-latency workflows. With experience in production AI systems, I will focus on monitoring, evaluation, and performance optimization. Let's discuss further details to initiate this project. Cheers, Vishal Maharaj

@abdf2010

Hello there, I reviewed your project LLM RAG Production Platform and understood the requirements at a high level. I focus on delivering clear, stable, and maintainable solutions aligned with the actual scope, I can work with JavaScript, Python, Software Architecture and follow a clean development process with proper structure and error handling. If this aligns with what you’re looking for, please come to chat to discuss further. Best regards