Python AI Engineer Required for LLM Deployment-Flexible -Long Term

@dfordesigners

Hi, there. I have carefully reviewed the project requirements for Python AI Engineer Required for LLM Deployment. It is clear that you are looking for a design that is not only visually modern but also highly effective at turning your visitors into active customers. My team and I specialize in UI/UX design and frontend development that balances aesthetic appeal with high-conversion functionality. We have a proven track record of helping businesses in United States improve their user engagement by focusing on intuitive navigation, clean interface design, and responsive performance using Graphic Design, Software Architecture, AI Development, Python. Our approach is to treat your interface as a tool for business growth. Before we start with any design work, we focus on understanding your specific user journey to ensure every element on the screen serves a purpose. We have helped other clients move from high bounce rates to improved retention by refining the user experience. To help me understand the specific design direction you are aiming for, I have one quick question: Are you looking to refresh an existing interface to improve current performance, or are you building a new user experience from the ground up? Knowing this helps me determine the best way to approach the wireframes and design logic. I am available for a discovery call to discuss your vision and current design needs whenever you are ready. Best regards Kausar and the Team

@ZohaibRoy

⭐⭐⭐⭐⭐ Python AI Engineer Ready to Build Your Next Big Project! ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a Python AI Engineer. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects. I will set up the AI core on production infrastructure, ensuring everything runs smoothly and efficiently within your budget. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in Python development, specializing in AI deployment, FastAPI, and Docker. My strong skills in GPU cloud services and vector databases will ensure a seamless production deployment. ➡️ Let's have a quick chat to discuss your project in detail. I can provide samples of my previous work, showcasing my experience in AI solutions. Looking forward to connecting with you! ➡️ Skills & Experience: ✅ Python (5+ years) ✅ FastAPI/Flask ✅ vLLM/TensorRT-LLM ✅ RunPod/AWS SageMaker ✅ Docker & Docker Compose ✅ Vector Databases (Qdrant, Pinecone) ✅ PostgreSQL with pgvector ✅ Redis (caching & queues) ✅ Git & CI/CD ✅ Linux (Ubuntu, shell scripting) ✅ API Development ✅ Web Scraping & Async Processing Waiting for your response! Best Regards, Zohaib

@komalshaikh92

Hello! I am ready to help you with Python AI Engineer Required for LLM Deployment, Exactly as you want. I have lots of experience in Graphic Design, Docker, FastAPI, Python, Software Architecture. Must check our profile to check amazing ratings and reviews: https://www.freelancer.com/u/komalshaikh92 I am available to chat or a call to discuss the project right now. Thankyou, Komal. Digitaurus Technologies.

@corpmember29

Hello, I am a Full-Stack AI Engineer with strong experience deploying production-grade AI systems using Python, FastAPI, Docker, GPU infrastructure, and modern LLM frameworks. Your project aligns closely with my background in building scalable AI platforms, RAG systems, vector databases, and cloud-based inference services. My expertise includes: • Production deployment of LLMs using vLLM and GPU cloud infrastructure • FastAPI microservices and API orchestration • RAG pipelines with Qdrant, pgvector, and hybrid search • Docker, CI/CD, Linux administration, and monitoring stacks • Redis caching, PostgreSQL, and distributed AI architectures • OpenAI-compatible APIs, embeddings, memory systems, and agent workflows For this project, I would focus on deploying a reliable AI infrastructure, optimizing inference performance, implementing scalable memory and retrieval services, integrating observability tools, and delivering complete API documentation and deployment runbooks. I can start immediately and am comfortable working within the 7–10 day timeline while supporting long-term platform growth afterward. I would be happy to discuss the architecture and deployment strategy in more detail. Thank you for your consideration.

@skalogir

Hello, I understand you need a production-focused Python AI Engineer to deploy and operationalize a complete AI ecosystem, including vLLM inference servers, FastAPI services, memory and RAG infrastructure, vector databases, GPU workloads, observability, API gateways, and containerized deployment pipelines. I have experience building and deploying AI platforms using Python, FastAPI, Docker, GPU-based inference environments, vector search systems, Redis, PostgreSQL, cloud infrastructure, and production-grade API architectures with monitoring, security, and scalability in mind. I can deploy the AI stack on production infrastructure, configure inference and memory services, implement RAG and orchestration layers, integrate monitoring and logging, document the environment, and support knowledge transfer to your development team. Q1: Are the RunPod GPU environments and model licenses already provisioned, or should infrastructure setup be included? Q2: Is Kong, NATS, Qdrant, Typesense, and the observability stack already partially deployed? Q3: What is the highest-priority deliverable for the initial 7–10 day phase? Best regards, Stratos

@KrisuTechkul

Hi, Krishna here from Delhi. Having completed numerous real-world AI projects and equipped with over 5 years of professional Python development expertise, I am fully qualified and eager to take on your project. My team and I at Krishna Kant specialize in crafting advanced AI solutions that precisely meet the unique and complex challenges businesses like yours face. We've successfully deployed production-level AI systems, including NLP frameworks and computer vision systems, making us the ideal partner for your AI ecosystem deployment. FastAPI, one of the main tools you require, is my specialization. I've deployed FastAPI-powered applications with ease, and this proficiency extends to other necessary tools such as Docker, PostgreSQL, and Redis – all essential skills for your project. Additionally, I am comfortable working with GPUs and CUDA on platforms like RunPod GPU pods or AWS SageMaker. Linux admin skills? Check! I have a solid command of Ubuntu and shell scripting ensuring efficient server management. My experience with comprehensive AI solutions includes everything from developing predictive models to generative AI. Notably, our utilization of RPA has consistently improved workflows in other businesses. Leveraging my solid understanding of cloud architecture (Kubernetes & Docker), I can ensure a seamless approximation of your proposed AI infrastructure while maintaining cost-efficiency.

@C0RETECHIES

Hello, What stood out to me is that this is not an AI application project. It's an AI infrastructure and deployment project. The challenge is building a reliable production ecosystem where inference, memory, retrieval, orchestration, monitoring, and GPU workloads operate as a cohesive platform rather than a collection of isolated services. We've built AI-powered systems involving FastAPI services, RAG architectures, vector databases, workflow orchestration, OpenAI integrations, knowledge retrieval, automation pipelines, and production-grade API ecosystems. While this exact deployment stack is specialized, it mirrors architectures we've implemented around AI services, retrieval layers, session management, and scalable backend infrastructure. My approach would be to establish the inference layer first, then build the memory service, retrieval pipeline, orchestration gateway, monitoring stack, and deployment automation. This ensures every service is observable, containerized, and production-ready before introducing higher-level AI workflows. What I particularly like is the separation between inference, memory, and orchestration. That architecture creates a foundation that can scale as additional models, agents, and creative tools are introduced. Best, Raksha

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@koolvishruth

With over 7+ years of proven experience in AI Development and Python programming, I'm not only equipped to handle the demanding nature of this project but help your team push boundaries and sets new milestones. As a top-ranked strategic problem solver, I have spearheaded 125+ successful projects that have leveraged Python, FastAPI/Flask, Docker, and much more - the exact skills needed for this role. I am intrigued by your approach to AI development that aligns greatly with mine - it's not just about the technology fait accompli; it's about making a positive impact. My portfolio is filled with AI deployment projects where I've successfully navigated critical aspects such as managing VRAM, setting up endpoints, containerizing services, logging, metrics, and tracing - a microcosm of what you require. Let’s connect

@durgesh8527

With over 12 years of experience and a well-rounded team of experts, we are well positioned to take on your Python AI engineering needs. Our expertise covers the deployment of large-scale AI ecosystems like what you've detailed in this project. Specifically, we have a robust proficiency in FastAPI and Flask - which is critical for your LLM deployment project - as well as TensorFlow and vLLM. We have also deployed on major cloud platforms such as AWS SageMaker. Dockerizing projects and integrating them with Docker Compose is another area that differentiates us. We understand the importance of efficient services, as we prioritize VRAM management, use of Flash Attention for optimum performance, and perform regular health checks to ensure stability and reliability. Also in our arsenal is familiarity with key software like Linux (Ubuntu) that'll be necessary for seamless integration. Our track record in PostgreSQL with pgvector, Redis (caching + queue), along with our Git & CI/CD, and Linux administration experience would be invaluable to your project. Moreover, our Python experience spans over 5 years professionally and we've successfully deployed several high-profile AI projects in the industry. When it comes to deploying AI at scale, GPIOct is no stranger to the task.

@DeveloperStation

Hi, We’ve developed production-level AI solutions using FastAPI, Python, and LLMs like OpenAI and Llama. One of our recent projects involved building a fully functional AI product with features like web scraping, document uploading, and fine-tuning LLMs for specific tasks. We also have extensive experience with CI/CD pipelines, Docker, and managing production servers on AWS and Azure. As a team, we can provide dedicated front-end and back-end developers, along with a DevOps expert, to ensure your product is robust and secure. Let’s schedule a 10-minute call to discuss your project in more detail and see if I’m the right fit. I usually respond within 10 minutes. Best regards, Adil

@PINESCRIPTERR

Hi, I reviewed "Python AI Engineer Required for LLM Deployment-Flexible -Long Term" and can help with it. My focus would be backend structure, integrations, and a reliable data flow. I can start by reviewing the existing access/files, then implement and test the requested changes. I can work around the listed stack/skills: Python Graphic Design Software Architecture Git Docker AWS SageMaker FastAPI AI Development. Quick infrastructure questions: 1. Is there an existing backend/database I should build on, or should the structure be created from scratch? 2. Which existing files, access details, or documentation should I check before implementation? 3. Which external services, APIs, or payment gateways need to be connected, and will test credentials be available? Best regards, Houssame

@krupnovdenis

Hello! We can provide an external AI engineering team for this production deployment scope. 1. Are you open to working with an external contractor or team for these tasks? 2. Which deployment blocks need to be covered first? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@quan9824

Hello, I’m interested in the Python AI Engineer role and available to start immediately for the 7–10 day production deployment phase. I have strong experience in Python backend engineering and production AI systems, including FastAPI services, Dockerized microservices, LLM integration, RAG pipelines, vector databases, and GPU-based deployments. I’m comfortable working in Linux environments with CI/CD, Redis, and distributed architectures. Relevant experience includes building and deploying: • FastAPI/Flask production APIs • Docker-based AI services and microservices • RAG pipelines using embeddings + vector DBs (Qdrant/pgvector) • Async workers and queue-based systems • LLM integration and inference workflows • Cloud/GPU deployments and performance optimization For your system, I would approach it as a modular AI infrastructure: • Deploy vLLM inference servers with OpenAI-compatible endpoints on GPU pods • Build Memory Service (FastAPI + BGE embeddings + Qdrant) • Create Brain Gateway for routing, context, and orchestration • Implement scraping + async processing workers • Set up hybrid RAG (vector + keyword search) • Containerize services with Docker and add observability (logs, metrics, tracing) I am production-focused (not research-only) and understand the importance of reliability, scalability, and GPU efficiency. Open to long-term collaboration if the fit is right. Best regards, Quan

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Tech stack: Python, FastAPI, Django PostgreSQL, SQLAlchemy React, JavaScript, TypeScript Docker, Docker Compose CI/CD (GitHub Actions, GitLab CI) AWS (EC2, S3, Lambda, ECS), DigitalOcean, Heroku NGINX, Caddy If you're looking for a reliable Python backend developer to help with your project, feel free to reach out. Your faithfully. Eng. Alema Akter

@zyeloncomputech

Hi sir, Thank you for giving opportunity for biding... we have gone through your requirements and we can do your LLM Deployment full AI ecosystem with LLMs, memory systems, search, video generation, and creative tools according to your exact requirements. Why You Need To Go With Us? And What Special you get with us. • Your vision = Our mission • Your idea + Our expertise = Winner on Web • Cutting edge web technology & design • Innovative, Cost effective & Customized service • Pragmatic Approach • Constant communication with clients • Consistent performance • On-time delivery • Maintenance of global quality standards • Your online business + Our experience = Your success Python, Data Analytics, Data Science Portfolio IoT Data Analysis for Dairy Refrigerator Temperature Monitoring Real-time Object Detection using OpenCV and YOLO Supply Chain Management System Enterprise Data Warehouse Implementation Cloud Data Lake Migration Web Application Development for Wind Turbine Performance Prediction Data Analytics Platform for Supply Chain Optimization AI Bill System AI Try Dress

@temoberishvili

Hello! I see you're looking for someone to help with deploying a large language model using Python and AWS SageMaker. With around 10 years of experience, I've worked on similar AI development projects that required strong Python skills and familiarity with Docker and FastAPI. Your goal of finding a team that values creativity and a positive work culture resonates with me. It's important to have a collaborative environment, especially when tackling complex AI challenges. Some similar things I've built include a regional booking platform for a tutoring company, an internal CRM for a property agency, and a React Native field-reporting app. I'm eager to discuss how I can contribute to your project. Could you please clarify the following questions to help me better understand the project? Q1: What specific LLM are you planning to deploy, and what are the main use cases you envision? Q2: Are there any existing systems or architectures that this deployment will need to integrate with? Q3: What level of graphic design support do you require for the project?

@Hrolgar

RunPod's cost model is going to hit you before the deployment does. Llama 3.3 70B at BF16 needs around 140GB VRAM, so a 2xH100 pod minimum just for that model server. Add Qwen 32B and DeepSeek R1 32B on separate pods and you're at $15-20/hr of GPU time before the gateway or observability stack even spin up. Worth knowing before scope is locked. I run vLLM with FastAPI microservices in my own homelab infrastructure, so I'm familiar with the gap between "running" and "stable in prod." The piece that needs most planning up front is Kong routing across RunPod's ephemeral pods, because instance URLs change on restart and your upstream config goes stale fast without dynamic service discovery baked in from day one. What I'd do: Docker Compose skeleton mapped to RunPod pod templates first, then get vLLM and tensor parallelism verified on the 70B before wiring anything else together. Memory Service and Brain Gateway behind Kong, Qdrant and Typesense for the RAG layer, FLUX and Whisper on separate pods to keep GPU scheduling clean, then Prometheus + Loki + Tempo with dashboards. OpenAPI docs exported from each FastAPI service, team walkthrough at the end. One milestone, full deployment, observability live, team trained. 10 days, $250. This is an indicative estimate from the brief; I'll give you a firm quote once the scope is locked. Are your RunPod API keys and storage volumes already provisioned, or are I starting from scratch there too?

@johnallenvillan1

Hi there, Your production AI stack needs vLLM on RunPod, FastAPI services, and reliable GPU deployment without research drift. I’ve spent the last 4 years solving exactly this type of problem: deploying LLM inference, memory services, and RAG pipelines into production with Docker, CI/CD, and observability. I’ve delivered a multi-model FastAPI inference platform with OpenAI-compatible endpoints and built a Qdrant-backed retrieval layer that stayed stable under real traffic. The risk here is orchestration fragility: GPU memory limits, health checks, service startup order, and context routing all need to work together cleanly. I’ll deploy the vLLM servers, wire the Brain Gateway and Memory Service, and connect scraping, hybrid search, and session context through clean API boundaries. I’ll containerise each service, add logging/metrics/tracing, and produce Swagger docs plus a runbook your team can use immediately. I keep production work isolated, documented, and reproducible so handoff is straightforward. Best regards, John allen.