Agentic Workflow Engineer (LLM / RAG / Product Integration)

@AwaisChaudhry

Hi, I’m Muhammad Awais. I will deliver a production-grade, embedded LLM assistant inside your visualisation app, with robust RAG pipelines, safe AI tool actions, and full telemetry, auth, and privacy controls. I’ll work with your product/docs and customer data sources to create a reliable workflow from prototype to release, focusing on real-world validation, performance, and governance. I’ll design data checks, structured input generation, and guided AI actions that fit your domain while meeting enterprise security and privacy needs. What are the top three production constraints (authentication model, data governance, latency/throughput targets) we must strictly meet for this delivery?

@irsolutions

With over a decade of experience in web and mobile development, specializing in cutting-edge technologies such as AI, ML, and blockchain, I understand the importance of delivering a reliable AI feature like the LLM assistant for your visualisation application. Your need for an Agentic Workflow Engineer who can seamlessly integrate this assistant into your existing product aligns perfectly with my expertise. In previous projects, I have successfully shipped AI features into production, working closely with backend engineering to ensure seamless integration and optimal performance. My experience in handling complex datasets and designing safe AI tool actions makes me well-suited for this role. I have a proven track record in developing AI solutions across various industries, including Fintech, HealthTech, and eCommerce. By leveraging my skills in backend development and AI integration, I am confident in delivering a high-quality, customer-ready AI feature for your project. If you are ready to take the next step in enhancing your product with an embedded LLM assistant, I am here to help. Please feel free to reach out to me to discuss how we can bring your vision to life efficiently and effectively.

@Feriver

Hello, I can develop a production-ready LLM assistant embedded in your visualization app, implementing RAG pipelines over product documentation and customer knowledge. The assistant will perform safe AI tool actions, clean and validate datasets, generate structured inputs, and guide workflows. I will ensure integration with authentication, permissions, logging, privacy controls, and performance optimization, with evaluation pipelines for quality metrics and regression testing. Previous projects include shipping LLM/RAG features with structured CSV/JSON data pipelines and backend architecture for production-grade AI features. Deliverables include fully integrated LLM assistant, safe tool execution, telemetry, and comprehensive documentation. Tech stack: Python, TypeScript, FastAPI/Node.js, RAG libraries, Electron integration if desktop required. Questions: Are there existing APIs or knowledge sources for RAG, or should ingestion pipelines be designed from scratch? Should the assistant support both desktop (Electron) and web simultaneously for MVP, or start with one platform?

@hireageek

Hi there, I am a top freelancer from California with a solid track record of successfully shipping LLM and RAG features into production. After reviewing your project requirements, I am confident that my 6+ years of software engineering experience aligns perfectly with your need for an Agentic Workflow Engineer who can seamlessly integrate an LLM assistant into your existing visualization application. My experience in designing safe AI tool actions and implementing robust evaluation workflows will ensure a reliable, customer-ready feature that meets your exacting standards. Having collaborated with product experts on similar projects, I am familiar with the complexities of handling messy datasets and can effectively navigate through them to deliver an optimized user experience. I am ready to discuss how we can move this project forward and help achieve your goals. What specific datasets will the LLM assistant be working with, and are there any existing tools you prefer for integration? Thanks,

@mubeenm5026

As a firm, Web Crest is deeply skilled in building intelligent solutions and one of our specialties includes AI and automation, which you are expressly searching for. Over the course of my decade plus career and with my 10-member expert team at Web Crest, I have amassed the kind of experience that sets me apart in this role: LLM/RAG features that have successfully navigated prototype to production, an avant-garde of complex dataset manipulation and integration using technologies such as Azure AI, and a solid understanding of desktop app integration through platforms like Electron. It is worth stating that I am well-suited to handle all aspects of this role from backend engineering to evaluation and more. In tandem with this, I bring a strong background in software production engineering that focuses on delivering production-grade features. To further buttress this point, let me mention that my systems never just 'look good' but are built to perform suitably for the task at hand. Lastly, it’s not just our technical firepower that has clients coming back time and time again—it’s also our collaborative approach. By working side by side with product/domain experts throughout the project, we ensure that our work encodes real-world workflows. This seamless partnership between us ensures not only the delivery of a reliable, customer-ready AI feature but also one

@efanntyo

Hi there, ⭐ I’m Efanntyo, a hands-on, production-focused engineer with 5+ years shipping robust AI-driven features in complex products. You’re seeking an Agentic Workflow Engineer to embed an LLM assistant inside a cross‑platform (desktop + web) visualization app, with RAG over product docs and customer data, safe AI tool actions, and a production-grade pipeline. I’ve led end-to-end delivery of LLM/RAG features into production, including authentication, permissions, telemetry, privacy controls, and performance optimizations, all while keeping messy datasets (CSV/JSON, etc.) clean and actionable. What I’ll deliver: - Embedded LLM assistant tightly integrated into your app, with a focus on reliability, maintainability, and a smooth UX for data cleaning, validation, and structured input generation. - Robust RAG pipelines over your documentation and customer knowledge sources, with efficient indexing, retrieval, and onboarding flows. - Safe AI tool actions designed for data checks, transformations, API calls, and template creation, with governance and auditing. - Production-ready evaluation and testing workflows: quality metrics, regression tests, telemetry dashboards, and CI/CD hooks. - Production-grade integration: auth, permissions, auditing, privacy controls, logging, error handling, and performance optimizations. - Collaboration with product/domain experts to encode real-world workflows and governance requirements. Experience highlights: I’ve shipped LLM-enabled features i

@planDapps

PRODUCTION GRADE AGENTIC AI: RELIABLE RAG & WORKFLOW AUTOMATION Industrial visualization requires deterministic AI tool actions, not experimental chat. With 12+ years of experience in custom software development and backend development, Plan D Studios specializes in shipping AI (Artificial Intelligence) features that are secure, governed, and production ready. Direct Answers & Experience: Shipped RAG: We recently deployed a production RAG pipeline for a logistics SaaS platform using Python and Typescript. I led the architecture, focusing on vector search optimization and regression testing. Complex Data: Expert at handling messy CSV/JSON datasets via "Safe Tool Actions" to ensure data integrity. Tech Stack: High proficiency in Azure OpenAI, API development, and Electron desktop integration. Production Standards: We implement full telemetry, logging, and privacy controls to meet enterprise AI governance. We do not outsource; our in house team handles all LLM Prompt Engineering and Retrieval Augmented Generation (RAG) architecture. Would you like to see our evaluation framework for ensuring 99% accuracy in structured data transformations? Regards, Haider

@Specgi

I'm Usman Haider and I'm genuinely excited about the possibility of collaborating with you as your Agentic Workflow Engineer. With my solid 5 to 8+ years of experience in the software engineering domain, coupled with specialized knowledge in LLM and RAG features, I am confident that I can develop and integrate the LLM assistant within your existing visualisation application efficiently. Additionally, having worked on Google Cloud Vision, I have hands-on experience tidying up messy datasets - an important skillset needed for this project. My diverse set of skills in languages like Python, R, SQL, Scala, JavaScript and using tools including AWS, Google Cloud Platform, etc. will enable a seamless workflow for embedding the LLM assistant. I understand the need to maintain high standards throughout the development lifecycle and my track record showcases that commitment by consistently delivering customer-ready AI features.

@vorasiddh4it

Hello, I’m a Senior Software Engineer (8+ years) specializing in production-ready LLM & Agentic workflow systems within enterprise software products. I’ve successfully shipped embedded AI assistants across desktop + web environments with secure, scalable architectures. ? Why I’m a Strong Fit ✔ Hands-on experience delivering in-product LLM assistants (not prototypes) ✔ Built RAG pipelines using vector search + structured knowledge layers ✔ Designed safe AI tool actions (data validation, transformation, API orchestration) ✔ Production hardening: auth, RBAC, telemetry, logging, privacy, governance ✔ Deep experience handling messy CSV/JSON datasets with schema inference & validation ? Technical Approach Agent Architecture Tool-based agent (function calling / structured output) Guardrails + prompt versioning Workflow encoding with deterministic fallback RAG Implementation Hybrid search (semantic + keyword) Chunking + embeddings optimization Evaluation pipelines (precision/recall, regression testing) Production Engineering Backend: Python / Node.js / TypeScript API orchestration & async processing Electron integration (if desktop) Azure OpenAI / Azure AI Search (if required) Full observability & performance tuning ? Relevant Delivered Project • Industrial Workflow RAG Engine – Secure knowledge assistant with role-based access I can share demo code, architecture diagrams, and live examples of shipped systems.

@andriik18

Hi there, I am excited about the opportunity to develop an embedded LLM assistant within your visualisation application. With my deep expertise in backend engineering and extensive experience shipping LLM features into production, I can deliver a reliable AI solution that enhances user interaction. I have over 7 years in software development, specializing in API integration and robust data handling, which aligns perfectly with your project goals. My strong background in working with complex datasets and creating evaluation workflows will ensure that the assistant functions seamlessly within your existing framework. I have successfully led the delivery of AI-driven features in past projects, focusing on performance optimization and safety regulations. I look forward to collaborating closely with your team to encode real-world workflows effectively. What specific challenges do you foresee in the integration process? Best, Andrii

@alejandroschultz

I build production LLM assistants embedded in data and visualisation products, helping users clean, validate, and transform messy CSV/JSON safely. I’ve shipped RAG-backed copilots inside existing UIs using Azure OpenAI, Python/TypeScript and vector stores, with guarded tool actions for data checks, structured inputs and API calls, plus auth, logging and performance tuning. For this role I’d align with your product team on target workflows, safety constraints and success metrics, then design evaluation, regression tests and telemetry to keep quality stable across releases. I can share examples of LLM/RAG systems I’ve delivered and I’m available for retainer or project-based engagement; shall we schedule a call to discuss fit and next steps?

@jeric5g

Hi there, I’ll architect an embedded LLM assistant inside your production app, with a safe RAG layer over your docs and customer data. I’ll prioritize reliability, auth, logging, privacy controls, and telemetry, delivering a production-grade flow from prototype to release. I’ll use a clear tool-action model for data checks, transformations, API calls and template generation, built into a robust CI/tests regime. Best regards, Jeric

@johnedwardl

Hi, I will develop an embedded LLM assistant for your visualization application, focusing on delivering a reliable, customer-ready feature. My experience includes shipping LLM and RAG features into production, ensuring robust backend integration and handling complex datasets efficiently. I will implement RAG pipelines leveraging product documentation and customer knowledge, ensuring safe AI tool actions for data validation and transformation. My approach includes building comprehensive evaluation workflows with quality metrics and regression testing. I prioritize security and performance, integrating authentication, permissions, and logging to meet enterprise data privacy standards. With over 8 years in software engineering, including extensive work on similar projects, I’m well-prepared to collaborate with your team to encode real-world workflows effectively. I have experience with the Azure AI stack and desktop integrations, which aligns closely with your needs. Could you share more about your existing application architecture? I’m ready to start immediately and look forward to discussing how we can bring this project to life. Thank you.

@patrickag1214

Hello, I will build and integrate a reliable LLM assistant within your visualization application, ensuring it effectively assists users in cleaning and validating datasets, generating structured inputs, and guiding workflows with safe AI tool actions. With over 7 years in software engineering, I've successfully shipped LLM features into production, including a recent project where I implemented a RAG pipeline for a data-driven application. My focus on robust backend architecture and seamless integrations has consistently delivered scalable solutions. To achieve your goals, I'll design the necessary RAG pipelines using your product documentation and customer knowledge sources. I'll implement safe AI actions for data checks and transformations, and establish thorough evaluation workflows to ensure quality metrics and regression testing are in place. Additionally, I will prioritize logging, permissions, and privacy controls to maintain compliance and optimize performance. I’d like to clarify: 1. What specific datasets will the LLM assistant primarily work with? 2. Are there existing workflows that the assistant needs to align with? 3. What is your timeline for the initial prototype delivery? I’m ready to start immediately and look forward to delivering a top-tier solution for your team.

@haidangvn2x

Hello, Greetings , Good afternoon! I am professional mobile coder with skills including AI (Artificial Intelligence) HW/SW, Retrieval-Augmented Generation (RAG), Typescript, JavaScript, API Development, AI Model Development, Python and LLM Prompt Engineering. Please contact me to discuss more about this project. Thanks & Regards

@TheDesignBloke

Hi, I am excited about the opportunity to create a robust LLM assistant integrated into your visualization application. With over 7 years of software engineering experience, I have successfully shipped multiple LLM and RAG features into production, ensuring they meet high performance and security standards. My expertise in designing safe AI tool actions and evaluating complex datasets through effective pipeline management allows me to deliver reliable solutions tailored to real-world workflows. I propose starting with a detailed assessment of your existing architecture to develop a timeline for implementation. This will include the integration of auth protocols, quality metrics, and collaboration with product experts. Could you share more about the preferred tech stack for the integration with your existing systems?