Senior AI Backend Engineer Needed

@MashoodurRehman1

I am a seasoned backend engineer with extensive experience in developing API services, particularly using Python and frameworks like FastAPI. My background includes turning advanced AI models into efficient, production-ready services, aligning well with your need for someone to wrap Large Language Models with robust endpoints. In my previous roles, I've designed, built, and optimized high-performance APIs, ensuring reliability through effective authentication layers, rate-limiting, logging, and CI/CD practices. I am proficient in containerization tools such as Docker, and have experience deploying applications across various environments. My expertise also includes working with AI frameworks like TensorFlow and PyTorch, crucial for integrating and experimenting with state-of-the-art AI architectures. I can deliver a version-controlled Python codebase with comprehensive test coverage and detailed Swagger/OpenAPI documentation, along with deploying and monitoring setups. I am keen to discuss timelines and delve into your repository to determine the best approach for your project's success.

@hirujiyu

Hi, Based on your requirements, I can help build a production-grade backend layer that exposes your LLM capabilities through secure, scalable, and well-documented APIs ready for both web and mobile applications. What I can deliver: ✔️ Python codebase with clean architecture and version control ✔️ Secure API endpoints wrapping your existing LLM models ✔️ JWT/OAuth authentication and access controls ✔️ Redis-based caching and request optimization ✔️ Docker, Docker Compose, and deployment scripts ✔️ OpenAPI/Swagger documentation ✔️ Unit & integration test suite targeting 90%+ coverage ✔️ Prometheus/Grafana monitoring integration ✔️ Structured logging and error tracking ❓ A few questions: 1. Are the LLM checkpoints self-hosted, or will they run through providers such as OpenAI, Anthropic, or others? 2. Do you require streaming responses (SSE/WebSockets) in the first release? 3. What is the expected request volume and latency target for production? I have experience building scalable Python backends, AI-powered APIs, and cloud-native systems with a strong focus on maintainability, observability, and performance. After reviewing your repository and infrastructure requirements, I can provide a detailed architecture plan, timeline, and milestone breakdown. Looking forward to discussing the project. Best regards, Team DDS..

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Robust APIs for Large Language Models with Python ❇️ Hi My Friend, I hope you're doing well. I just reviewed your project requirements and see you are looking for a backend engineer to develop APIs for Large Language Models. You don't need to look any further; Zohaib is here to help you! My team has completed over 50 similar projects focused on API development. I will create efficient Python-based endpoints that expose LLM features for your web and mobile clients. I will ensure clean, test-covered code using FastAPI, manage authentication, rate-limiting, and logging while implementing CI/CD and containerization. Additionally, my experience with AI stacks like TensorFlow and PyTorch will help us explore alternative architectures effectively. ➡️ Why Me? I can easily build your API service as I have 5 years of experience in backend development, specializing in API design, Python programming, and performance optimization. My expertise includes working with FastAPI, Docker, and CI/CD practices. Not only this, but I also have a strong grip on monitoring tools and testing frameworks. ➡️ Let's have a quick chat to discuss your project in detail and I can show you samples of my previous work. I look forward to discussing this with you!

@OutsourceMan

Given our broad and illustrious background in web and app development at CnELIndia, we're perfectly positioned to tackle the full scope of your project raging from API design to implementing complex functionality ensuring that you get a truly robust service. With over 18 years of industry experience, we've developed solid proficiency in Python and expertise in backend technologies such as FastAPI. This means writing clean, test-covered code that is essential for maximum stability. Building on our impressive portfolio of successfully delivered projects, the trust bestowed on us by over 743 satisfied clients can attest to our meticulous nature in delivering 90+% test coverage, building monitoring hooks using Prometheus/Grafana for effective performance tracking, as well as providing concise Swagger/OpenAPI docs -criteria that are integral to this work. Summing it up, our consistent track record of quality service delivery speaks volumes about our ability to handle complex AI services like yours satisfactorily. As we take on your project, not only will we meet your expectations but surpass them.

@kamran2012

Good to see this project, I will build the FastAPI service that wraps your existing LLM checkpoints behind versioned REST endpoints, complete with auth layers, rate limiting, and Prometheus/Grafana monitoring hooks. You will get a Dockerized codebase with CI/CD pipelines, Swagger docs, and test coverage at or above your 90% target. On a similar LLM API build, adding response caching at the inference layer cut median latency by roughly 40%, which made a real difference once mobile clients started hitting the service concurrently. I will profile yours early to find the same opportunities. Questions: 1) Are the LLM checkpoints hosted on your own GPU infrastructure, or are you using a managed service like AWS SageMaker? 2) Do you already have a staging environment, or will I set that up as part of the first milestone? Ready to start whenever you are. Kamran

@gotucat

Hi, I’ve reviewed the project and the main requirement is to turn LLM capabilities into a robust, production-ready API service using Python. You need clean endpoints that securely expose features to web and mobile clients, with solid tests, clear docs, and reliable observability. I will design and implement the endpoints with a focus on software architecture and API development, wrapping your LLM checkpoints behind REST or gRPC interfaces. I’ll include authentication, rate-limiting, logging, CI/CD pipelines, Docker deployments, and concise Swagger/OpenAPI docs plus robust unit and integration tests. Expect reliable, clean work with responsive communication and easy management; I’ll proactively tune performance, plan caching strategies, and provide clear monitoring hooks. Let’s discuss here now.

@corpmember29

Hello, {{{ I HAVE CREATED SIMILAR BEFORE AND I CAN SHOW YOU }}}} I have carefully reviewed your requirements for a production-ready AI backend platform. I have 10+ years of experience in Python, FastAPI, AI/LLM integrations, backend architecture, REST APIs, microservices, and scalable cloud deployments. I can build secure, high-performance Python APIs around your LLMs with FastAPI, authentication, rate limiting, caching, logging, Docker, CI/CD, monitoring, and comprehensive OpenAPI/Swagger documentation. The solution will be production-ready, scalable, and optimized for low latency and high availability. AI Backend Platform AI API Services LLM API Development REST / gRPC Endpoints Authentication & Authorization Rate Limiting API Documentation AI Processing LLM Integration Prompt Processing Model Inference AI Pipeline Management Response Optimization Backend Infrastructure FastAPI (Python) Docker & Containerization CI/CD Pipeline Caching & Performance Optimization Monitoring & Logging Testing & Deployment Unit & Integration Testing Swagger/OpenAPI Documentation Staging & Production Deployment Prometheus & Grafana Monitoring Additional Features Secure & Scalable Architecture High Availability Performance Profiling Future AI Model Integration 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

@gutibs

Hi! This is something we can definitely take on. The brief is clear on the deliverables, so a couple of things that would help me scope it properly: do you already have the LLM checkpoints ready to wrap, or is selecting/fine-tuning the model part of the work? And what does the current infra look like — are we deploying to AWS, GCP, something else, or is that still open? We'd build this in FastAPI with Docker from day one, full OpenAPI docs, auth, rate-limiting, and monitoring baked in — not bolted on later. Test coverage at 90%+ is standard for us on this kind of service. Happy to dig into the repo and timelines once we clear those two points. Gustavo & the DoTheCode team

@C0RETECHIES

Hi there, You need to wrap your LLM checkpoints into a production-grade, scalable API service. This involves building a reliable backend that handles authentication, rate-limiting, logging, and monitoring, serving as the robust interface between your models and your client applications. Technical approach: We will build a containerized Python service using FastAPI for its performance and built-in OpenAPI documentation. The service will use Docker for portability and a CI/CD pipeline for automated testing and deployments. JWT for auth and a Prometheus endpoint for metrics are standard for this architecture. Core modules: This includes the API router, a dedicated inference engine managing model calls (with potential for request batching), Pydantic-based data validation, middleware for security, and a full test suite to ensure the required >90% coverage. Relevant systems: We have architected and deployed several production backends that serve AI models, including a conversational AI backend that handles intent detection, memory, and real-time external API calls. Implementation strategy: We’ll begin with an MVP-a single, containerized endpoint with CI/CD for early validation. From there, we'll layer in advanced features, robust monitoring, and performance optimizations for production readiness. Regards, Rohit

@dfordesigners

Hello, I can help you build a robust, production-ready service around your Large Language Models. My focus will be on designing and developing clean, well-tested Python-based endpoints using FastAPI to expose your LLM features reliably to your clients. I have experience with API development, containerization using Docker, and setting up CI/CD pipelines, ensuring your service is scalable and maintainable. I'm comfortable with monitoring hooks and performance tuning to keep your AI service running smoothly. Can you share more details about the existing LLM checkpoints and your preferred deployment environment? Best regards, Kausar & Team

@tilalHusain

Hey there, Turning an LLM prototype into a dependable product requires more than connecting a model to an endpoint. The real work is building an API layer that stays fast, secure and stable when real users start hitting it. I can help you build the backend foundation around your existing LLM checkpoints with clean Python services using FastAPI, strong authentication, rate limiting, structured logging and production-ready deployment workflows. My focus would be creating well-tested REST or gRPC endpoints, containerizing the application with Docker, setting up CI/CD pipelines and adding monitoring through tools like Prometheus and Grafana. I also understand the importance of profiling latency, improving response times and implementing caching strategies as usage grows. I write maintainable code with proper unit and integration testing, clear API documentation and a structure that makes future AI experiments easier to manage with frameworks like PyTorch, TensorFlow or Scikit-Learn. I’d like to review your current repo and architecture, then suggest the best path to get the first milestone live. Let’s discuss the implementation plan and timeline. Best Regards, Tilal

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced Python & AI Backend Developer with 10+ years of experience. I’m excited to partner with you and can dive into your project immediately. I have rich experience in Python, FastAPI, REST APIs, Docker, CI/CD, PostgreSQL, Redis, AI integrations, and cloud deployment. I understand you need a production-ready backend that exposes your existing LLMs through secure, high-performance APIs. I can build scalable FastAPI/gRPC endpoints with authentication, rate limiting, logging, Docker deployment, comprehensive testing, OpenAPI documentation, and monitoring integrations, while optimising for performance, caching, and future scaling. I am skilled in Python, FastAPI, Docker, PyTorch, TensorFlow, REST/gRPC APIs, Redis, and Prometheus/Grafana. I’m ready to start immediately and would be happy to discuss your architecture, deployment environment, and project timeline. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@komalshaikh92

Hi there, You want the LLM checkpoints wrapped behind production-grade endpoints with ≥90% test coverage and monitoring hooks from the start, not just a working API, so testing and observability need to be built in alongside each endpoint, not added at the end. My approach would be to first design the FastAPI structure wrapping your existing LLM checkpoints behind REST endpoints with auth and rate-limiting in place, then build out unit and integration tests targeting 90%+ coverage as each endpoint is completed, followed by Dockerizing the service with CI/CD and Prometheus/Grafana monitoring hooks for uptime and latency tracking before moving into performance tuning and caching. I have experience developing full-stack applications with Python backends, REST API design, Docker, and Git-based workflows, with a strong focus on clean, well-tested, maintainable code. Once the NDA is in place and I review the existing LLM checkpoints and infrastructure, I can provide a detailed milestone plan and confirm the best approach for scaling and monitoring.

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings

@ashinnd84

Hello there, we are a team of senior AI ML Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@KarthikTechCon

Hi, Senior AI Backend Engineer: With 15+ years of experience, I specialize in transforming Large Language Models into robust, production-ready services. I will deliver highly optimized Python-based APIs that enhance your web and mobile applications. Execution Strategy: - Design and build REST (or gRPC) endpoints using FastAPI for seamless LLM integration. - Implement security features, including authentication layers and rate-limiting, to ensure API reliability. - Utilize Docker for containerization and create deployment scripts for efficient staging and production processes. - Develop comprehensive unit and integration tests achieving ≥90% coverage, alongside detailed Swagger/OpenAPI documentation. Key Deliverables: - A version-controlled Python codebase encapsulating existing LLM checkpoints. - Dockerfile and deployment scripts for smooth transitions to production. - Monitoring hooks integrated with Prometheus/Grafana for real-time performance tracking. - Ongoing performance tuning and caching strategies post-launch. Quality & Performance: - Commitment to high testing standards with extensive coverage to ensure code reliability. - Focus on API scalability to handle increased loads effectively. Timeline & Next Steps: - Initial milestone delivery in approximately 4 weeks, followed by iterative enhancements. I'm available to dive into the repo and discuss further. Best Regards, Karthik B Resonite Tech

@Aaleen110

I understand you require a seasoned backend engineer to build a rock-solid, production-ready service exposing LLM features via Python-based API endpoints. I have previously designed and deployed a high-throughput API for a machine learning service, reducing latency by 30% and handling 100,000 requests daily. I will deliver a robust API using FastAPI, implementing secure authentication, rate-limiting, and comprehensive logging. Containerisation with Docker and a CI/CD pipeline using GitHub Actions will ensure reliable deployments. The service will be built with Python 3.9+, leveraging libraries like Pydantic for data validation and SQLAlchemy for database interactions. What is the expected latency target for individual LLM inference calls to the API? Ready to start as soon as you confirm scope.

@techsty2014

This looks like a great fit, We will build your LLM service layer in FastAPI: versioned REST endpoints, auth, rate limiting, Dockerfile for staging and production, and Prometheus/Grafana monitoring hooks. For the API design, we will place an async queue between the client and the LLM inference layer. This prevents long model calls from blocking the endpoint. It also gives us a natural place to add response caching, which cuts redundant token usage and drops latency for repeated queries. A couple of quick things to confirm: 1) Are the LLM checkpoints hosted on your own infrastructure, or are you calling a managed provider (OpenAI, Anthropic, etc.)? 2) Do you need gRPC alongside REST, or is REST sufficient for your mobile and web clients? The number quoted here is a starting estimate. Looking forward to your response. Best regards, Faizan

@krupnovdenis

Hello! We can build the backend API layer for your LLM-based product. 1. Is there an existing repo or service we should connect to? 2. Which part is the first priority: API, deployment, or monitoring? — 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!