Senior Full-Stack AI Engineer

@irsolutions

With over a decade of experience in full-stack AI engineering, I understand your need to expand your product with a retrieval-augmented generation (RAG) layer, taking it from proof-of-concept to a polished, production-ready service. My background in high-complexity systems, such as scaling for over 1 million users, directly applies to architecting and coding the RAG-powered backend for vector store integration, model orchestration, robust APIs, and monitoring hooks. A strategic insight for ensuring scalability and reliability is to implement robust monitoring mechanisms to track API performance and system health. I have successfully built and scaled high-performance systems, including a Telegram Mini App serving over 1 million users, showcasing my ability to handle complex backend development projects like yours. I encourage you to reach out to discuss how we can collaborate on driving your project forward. Let's connect to strategize the roadmap for seamlessly integrating the RAG layer into your product and delivering clean, well-documented code with efficient deployment scripts.

@corpmember29

Hello, I have reviewed your requirements for a Senior Full-Stack AI Engineer role focused on building a production-ready RAG system. I have 10+ years of experience in software engineering, working across backend architecture, AI integrations, APIs, cloud deployment, and scalable full-stack applications. I have experience designing RAG pipelines, LLM integrations, vector databases, AI-powered search systems, API services, and production automation workflows. For your project, I can help with: • RAG architecture design and backend implementation • Vector database integration and knowledge retrieval pipelines • LLM orchestration, prompt workflows, and evaluation • Secure and scalable REST APIs • Docker-based deployment and CI/CD setup • Database design, monitoring, and performance optimization I focus on writing clean, maintainable code with proper documentation and production-ready deployment practices. I can collaborate effectively with your frontend team and provide reliable APIs for seamless integration. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY AND PROVIDE ASSISTANCE FROM ZERO TO PUBLISHING ON STORES. I am ready to discuss your current architecture, data sources, and RAG requirements to start building a robust AI solution. I eagerly await your positive response. Thanks

@sharmarahulraman

Hi, I'd love to help build your production-ready RAG platform. My approach is to develop a scalable backend with vector database integration, LLM orchestration, secure APIs, monitoring, and a Docker-based deployment pipeline that your frontend team can integrate with seamlessly. With 8+ years of experience in Python, Node.js, RAG systems, LLM integration, vector databases (Pinecone, Weaviate, Chroma), REST APIs, Docker, and Kubernetes, we've built AI-powered applications with a strong focus on performance, scalability, and maintainability. A few questions: - Which LLM and vector database are you currently using or planning to use? - Do you already have a RAG proof of concept, or will this be built from scratch? - What's your preferred cloud platform (AWS, Azure, or GCP)? I can start immediately and would be happy to discuss the architecture and implementation plan.

@MashoodurRehman1

I am a seasoned Full-Stack AI Engineer with extensive experience in developing retrieval-augmented generation (RAG) systems. My professional background includes architecting and implementing scalable backend solutions, specifically tailored to integrate vector stores and manage model orchestration. With a clear focus on creating robust APIs and embedding essential monitoring hooks, I ensure that applications are both reliable and efficient. In my recent projects, I have successfully turned RAG-based prototypes into production-ready services, complete with clean, maintainable code and automated CI/CD pipelines. My expertise in Docker, Kubernetes, and Terraform allows me to streamline deployments via a single command, facilitating seamless transitions across staging and production environments. Past work not only meets stringent performance benchmarks but also emphasizes smooth integration with frontend teams. I am keen to discuss how my skills align with your project's needs and can provide links to relevant repositories and live demos upon request. Please let me know if there are specific aspects you’d like to explore further.

@teodorgeorge

Hello, I work on backend-heavy full-stack systems where private knowledge search, API design, and production deployment matter. For AI Chatbot Development, I can shape the RAG layer around vector search, model orchestration, and clean endpoints your frontend team can consume. I will turn the proof of concept into a service with fast API responses, monitoring hooks, tests, and clear deployment scripts. The AI Model Development work will be tied to reproducible staging and production runs, with Docker support for single-command deployment and CI checks on every merge. Best regards, Teo

@mahads31

Hi, Could you share more about the specific use cases you envision for the RAG layer? I can help take your proof-of-concept to a polished, production-ready service. With over 5 years of experience in backend development, I specialize in architecting and coding end-to-end applications. I can integrate vector stores, ensure model orchestration, and create robust APIs that meet your performance benchmarks, like 300 ms response times. I also prioritize clean, well-documented code and streamlined deployment using Docker or Kubernetes, ensuring easy reproduction across environments. I’m eager to showcase my recent projects and discuss how we can work together to bring your vision to life. Best Regards

@naminghn

Hi there, I'm eager to dive into developing the RAG-powered backend for your application. With my extensive experience in building robust APIs, integration with vector stores, and deploying applications via Docker, I can ensure a seamless orchestration that meets your benchmarks of performance and reliability. Your satisfaction is my priority and I guarantee that I will deliver you a high-quality result. Regards, Ali

@Yunsoft

Hello , I just saw your project regarding Senior Full-Stack AI Engineer. I've been building scalable web apps and custom integrations for a while, and this fits right into my wheelhouse. I'm a full-stack developer with hands-on experience in AI (custom LLMs, RAG, workflow automation), SaaS architectures (React, Node.js), and Web3 integrations. Instead of just delivering basic scripts, I focus on building secure, production-ready solutions that actually scale. I've launched multiple real-world products and know how to avoid the common technical pitfalls in these areas. Let's connect so we can go over your exact needs. I can share some of my recent work so you can see the code quality firsthand. Thanks, Emre

@shawanay

Good day, I see you’re looking for an experienced engineer to build a production ready RAG powered backend with LLM integration. I can develop scalable APIs, and create reliable end to end applications with clean documentation and deployment workflows. With experience in RAG systems, backend development, Docker/Kubernetes, and CI pipelines, I can build optimized solutions with strong performance, testing, and production readiness. Could you share your current tech stack and preferred LLM/vector database setup? Regards, Shawana

@C0RETECHIES

Hi there, I understand you're looking to build a production-grade RAG service. Operationally, this means an automated pipeline will ingest your knowledge base, chunk and embed the content into a vector store, and expose a low-latency API. When queried, this API will retrieve relevant context, augment an LLM prompt, and return a source-grounded response for your frontend team to consume. Technical approach: We'll use Node.js (Fastify) for a high-performance API backend. For the core RAG pipeline, we'll integrate a vector database like Pinecone or Chroma and use LangChain for orchestration. The entire service will be containerized with Docker, with a CI/CD pipeline in GitHub Actions for automated testing, linting, and deployment. We'll manage infrastructure via Terraform. Core modules: - Data Ingestion & Embedding Pipeline: Handles automated document processing, chunking, and upserting into the vector store. - RAG Query Service: Manages incoming queries, similarity search, context retrieval, and prompt construction. - API & Monitoring Layer: Provides robust, documented endpoints and hooks for performance and cost monitoring. Relevant systems: We built an AI-Powered Slack Assistant using LangChain with conversational memory. This system retains context to generate structured workflow plans, a process architecturally similar to a RAG workflow for retrieving relevant information. Implementation strategy: We will begin with an MVP focused on the core data ingestion and query pipeline. Once the API is functional, we will benchmark and iterate to meet the <300ms latency requirement under load before hardening the system for production deployment. Regards, Rohit

@krisk63

Hi, this is Kris from McKinney, Texas. I've reviewed your project requirements and understand the need for a seasoned engineer to develop a retrieval-augmented generation (RAG) layer, focusing on backend development and end-to-end application building. The key challenge lies in seamlessly integrating the RAG-powered backend with the vector store, model orchestration, APIs, and monitoring hooks to ensure a reliable and efficient system. My approach involves architecting and coding the backend with a strong emphasis on performance optimization, seamless integration, and documentation for easy consumption by the frontend team. I will prioritize clean code and deployment scripts to facilitate system reproducibility across different environments. A few additional questions: Q1: Are there any specific technology stack preferences or constraints for this project? Q2: Is there an existing knowledge base structure that the RAG layer needs to integrate with? Q3: What are the key metrics for monitoring the performance and success of the RAG-powered backend? Best regards, Kris Kramer

@mubeenm5026

With Web Crest's extensive AI-focused capabilities and in-depth knowledge of backend development, we are the perfect fit to drive your project from concept to a robust, production-ready service. Building a seamless retrieval-augmented generation (RAG) layer is our forte; our expertise encompasses everything from vector store integration and model orchestration, to API development and monitoring hooks. Your emphasis on maintaining end-to-end productivity aligns with our approach, ensuring minimal friction for your frontend team in consuming the final product. In addition to building clean, well-documented code and deployment scripts, rest assured we understand the necessity of performance efficiency and maintainability by designing systems that can be reproduced across multiple environments reliably. We guarantee adherence to your acceptance criteria; with an extensive skillset in Docker/Kubernetes/Terraform, we can streamline your deployment process, forging a system that's ready for both staging and production instantly. One distinct advantage with us is our business-centric approach. We're not just engineers who excel at their craft; we're experienced end-to-end solutions providers who look through the lens of scalability and user satisfaction.

@jaroslavcaprata

Building a production RAG service is less about connecting an LLM to a vector database and more about designing a reliable retrieval pipeline with predictable latency, observability, and maintainable infrastructure. Meeting a 300 ms response target requires careful indexing, caching, efficient retrieval, and well-defined orchestration rather than simply selecting the right model. My background is in full-stack development, AI-powered applications, backend systems, API design, and cloud deployment. I can build a scalable RAG architecture with vector store integration, robust REST APIs, monitoring, containerized deployment, and CI/CD pipelines that support reproducible staging and production environments. I focus on writing clean, well-tested code with clear documentation so the service is easy to maintain and extend as your knowledge base grows. I'd start by reviewing your current proof of concept, validating the retrieval strategy and performance bottlenecks, then incrementally hardening it into a production-ready service with automated testing, deployment, and monitoring. Could you share which LLM provider and vector database you're currently using, and whether the 300 ms benchmark includes model inference time or only retrieval and API processing?

@MohsinCodeCraft

Hi, I can help build a production-ready RAG platform from architecture to deployment, including vector database integration, LLM orchestration, secure APIs, monitoring, and scalable backend services. I have experience with AI applications, backend engineering, Docker-based deployments, CI/CD pipelines, and building reliable systems that connect private knowledge bases with LLMs. I’ll deliver clean, documented, and maintainable code with proper testing and deployment workflows.

@rbtech1984

Hi there, A production ready RAG powered backend can be architected and built, handling vector store integration, model orchestration and robust APIs so your team can query large language models against your private knowledge base with minimal friction. Quick questions before starting: Is there already a preferred vector store, like Pinecone, Weaviate or pgvector? Should deployment target Docker Kubernetes, or a simpler containerised setup first? Do you already have a benchmark load defined for the 300 millisecond target? Solution will include clean well documented backend code, CI pipeline setup with unit tests and linting, single command deployment scripts, monitoring hooks for reliability, and an end to end reproducible system across staging and production, built to hand off smoothly to your frontend team without friction. Relevant experience includes taking RAG prototypes into real production services with strong backend architecture and reliable deployment pipelines. Happy to share related repos and discuss further in chat. Best regards Farhin B

@ska234

Hi. To build your RAG layer, I’d start with a clean backend architecture around fast retrieval, model orchestration, and stable APIs so the frontend team can plug in without friction. I’d use Python, FastAPI, PostgreSQL/pgvector or Pinecone, background jobs for indexing, and Dockerized deployment with CI checks for tests and linting. The focus will be low-latency query flow, cache-aware retrieval, and monitoring hooks so the service stays within your 300 ms target under load. I’ll also make the deployment reproducible with clear scripts for staging and production. As a Senior AI Engineer, I have mastered RAG pipelines, vector databases, API design, and production deployment, and have strong experience in backend systems and end-to-end AI products. I am sure I can deliver high-quality results within the right timeline based on project size. Let’s get in touch and discuss more. Thanks.

@ammarahmed20

With a robust background in AI Chatbot Development, API Development, and Backend Development, I'm confident I'm the complete package you need for your project. As a seasoned engineer with over 6 years of experience, my track record is filled with projects just like yours; from ideation to a polished, production-ready service. Specifically, I've successfully developed AI systems with retrieval-augmented generations (RAG) layers enabling them to query large language models against private knowledge bases similar to the task at hand. Furthermore, what sets me apart is not just my technical prowess but also my 'production-first' approach. I understand that ultimately it's tangible products that speak volumes about their creators. And I like to let my products do the talking. Hence you can expect ownership right from architecture choices tailored to your unique context until successful deployments and solid documentation so that your team has no friction in adopting and extending the system once complete.

@nemanjam61

Hi there, I’m Nemanja from Serbia. I can take your RAG concept from prototype to a production-ready backend with reliable retrieval, model orchestration, clean APIs, and reproducible deployment. I’ll design the service around an appropriate vector store and retrieval pipeline, connect the LLM layer, implement robust API endpoints, and structure the application so your frontend team can integrate it without unnecessary complexity. Performance will be measured rather than assumed. I’ll profile the critical paths, optimize retrieval and model-related overhead where possible, and validate the API against your 300 ms benchmark. I’ll also set up automated tests, linting, logging, monitoring hooks, and a CI workflow so every merge is validated before deployment. For delivery, I can containerize the application with Docker and provide deployment configuration suitable for staging and production, with Kubernetes or infrastructure automation where the environment requires it. My experience includes Python, FastAPI, REST APIs, Docker, AI integration, backend development, databases, and automation. I can start immediately and would first review your existing RAG prototype, knowledge-base structure, model requirements, and benchmark environment. Looking forward to working together.

@Muhammadzeesha59

Hello Sir/MAM I am a skilled Full Stack developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms, Linux , Windows , Cloud , Azure . I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning “Computer Vision ”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards