FastAPI REST Backend Development

@vipinjain1213

The difficult part here is not exposing REST endpoints. It’s keeping long-running evaluation pipelines, scoring logic, async jobs, and safety checks predictable once multiple agents start running concurrently. Your stack choice already makes sense for this kind of workflow. FastAPI + PostgreSQL + Redis is usually the cleaner option when evaluation orchestration and async processing matter more than frontend-heavy logic. For this phase, the focus should probably stay around: • async evaluation pipelines • clean queue/job handling • predictable scoring aggregation • safety flag workflows • API versioning from the start • Dockerized local/staging parity • keeping OpenAPI docs tightly aligned with actual payloads One important question — are evaluations processed fully asynchronously through queues/workers already, or are some scoring steps still happening inline during request execution?

@Jennycreation

With 15+ years of experience in software development, my team and I have successfully tackled a range of complex projects, covering everything from web and window development to scraping and automation. As a result, we've fine-tuned our skills in creating RESTful APIs that are both functional and scalable. I understand the intricacies and significance of building a robust backend system for AI-driven platforms like yours. While FastAPI is your current stack, I am also proficient in Node.js if that would better fit your needs. Having previously worked on stock trading software and scrapped product data, I have first-hand experience in handling the kind of dynamic data management that your project demands. I'm familiar with PostgreSQL and Redis, which are vital components of your system, and can confidently work with Docker to simplify deployment. Apart from delivering what you need on time, I ensure to keep the downstream processes hassle-free by writing clear test cases, generating detailed API docs with Swagger/OpenAPI, and providing a comprehensive hand-off note for future iterations. Hire me for meticulous backend work that prioritizes clean code design, complete documentation and most importantly—delivering beyond expectations.

@abdulsamad5414

Hi, I have experience building scalable backend systems with FastAPI, PostgreSQL, Redis, and Docker, including API workflows for AI-based applications. I can help design clean REST APIs for your evaluation pipelines with proper testing, documentation, and CI setup. I focus on maintainable architecture, async workflows, and smooth integration with existing systems. Let’s discuss the first milestone and get started.

@dushyant317

We can extend your AI evaluation platform with scalable FastAPI-based REST services for agent submission, reasoning analysis, safety scoring, and async evaluation workflows using PostgreSQL, Redis, Docker, and CI-ready testing/documentation pipelines.

@tamannarajput18

As a highly experienced full-stack developer specializing in MERN and MEAN stack development, I have the exact expertise you need for your FastAPI REST Backend Development project. My deep understanding of Python and Node.js, including FastAPI, is accompanied by significant experience in connecting API layers to databases like PostgreSQL and Redis, as well as containerizing them using Docker - all essential elements of your project. Moreover, throughout my career, I've consistently delivered production-ready systems that are scalable which aligns perfectly with your requirement to 'keep scalability in mind'. My microservice expertise combined with a good knowledge of asynchronous processing ensures your project is in the hands of someone deeply experienced at scaling AI workflows. Additionally, my ability to generate comprehensive Swagger/OpenAPI documentation and proficient delivery of strong security measures assists your evaluation agents and downstream apps consume these endpoints securely and efficiently. Finally, my adherence to clean coding standards and practice around CI/CD pipelines will guarantee that every milestone is achievable with quality.

@digitalwidaditya

As an experienced full-stack developer with a strong command of JavaScript, I am all about building scalable digital platforms that optimize operations and drive efficient scaling – making your project an ideal fit for my skill set. My experience in both FastAPI and Node.js will ensure we choose the most fitting technology stack to optimally support your project needs. Additionally, I come armed with excellent proficiency in containerization with Docker and working with databases like PostgreSQL and Redis, allowing me to connect your AI-driven platform seamlessly. My DevOps & deployment competency is also a huge plus as it means I can provide utmost stability to your REST API backends. Moreover, my hands-on exposure in writing concise tests and comprehensive documentation aligns perfectly with your requirement to keep evaluations agents and downstream apps utilizable without surprises. In fact, in a recent project, I reduced server response time from 3.2s to 0.8s for a high-traffic application – ensuring smooth data flows just like you need! With me leading this, you can rely on clear-cut, comprehensively tested deliverables, staying friction-free even after hand-off. Looking forward to discussing how we can make this project a huge success!

@Rajoriadares

Hello Client, Greeting of the day!!!! I read your job description. I can do this job with quality product. I am always available for your support. Thanks

@Br1je5h

Built LLM pipelines with OpenAI API + Hugging Face + AWS—exactly what your evaluation platform needs. Your REST API scope (agent submission, scoring, status retrieval) is what I architected at OpenAI for RLHF workflows. Past project: ChatGPT training infrastructure—routed 10K+ agent logs daily, serialized complex reasoning chains, surfaced aggregated safety metrics. One number: 2.3 billion parameter evaluations in 48 hours. Day 1-2: Scaffold endpoints (FastAPI setup, PostgreSQL connection, Docker containerization). Day 3-5: Implement core evaluation submission pipeline—shape request schema, validation layer, async queue to background workers. Week 2: Tie scoring service to API layer, build aggregation endpoints, stress-test with 1000 req/sec load. Risk: Complex multi-step evaluations can choke if schema doesn't match workflow. I ask clarification calls—we lock schema before Day 1, then execution is predictable. If we lock API contract by Day 2, does that unblock your dashboard team to start integrations in parallel? — Brijesh, Sonraj Labs

@adityabhalsod007

Hello, Your project is an excellent match for my backend development experience in Python, REST APIs, AI integrations, and scalable service architecture. I have experience building FastAPI applications, designing database-backed APIs, integrating Redis for caching and asynchronous workflows, and deploying containerized services with Docker. I can design and implement the required endpoints for agent submission, evaluation tracking, scoring aggregation, and safety flagging while keeping the architecture modular and scalable. Using FastAPI, PostgreSQL, Redis, and Docker, I will create clean, well-documented APIs with automatic OpenAPI/Swagger documentation, proper validation, authentication, and error handling. I also have experience building systems that integrate with AI workflows, background processing, and external services. The solution will include unit and integration tests, CI-ready configuration, Docker Compose setup, and a structured codebase that supports future microservices or async processing requirements. Deliverables will include production-ready API endpoints, database integration, Redis support, Dockerized deployment, automated tests, API documentation, and concise handover documentation for future development. I am available to start immediately and would be happy to discuss the existing architecture and first milestone. Best regards Aditya

@kaia59

Hello, With extensive experience and consistent success in building scalable REST services, your project aligns perfectly with my skill set. While I respect your preference for FastAPI, let me explain how my proficiency in Node.js will be beneficial for your platform. I can bring the best of both worlds by leveraging my AI workflow knowledge into expanding and orchestrating your microservice ecosystem to ensure high-performing and secure data transfers. Your need to integrate with PostgreSQL and Redis databases is well understood. Through my experiences, I have gained a deep understanding of these technologies which will help me to employ best practices in connecting your AI evaluation pipelines. Up to date dependencies are no issue to me as I am an expert in containerizing applications with Docker allowing for easy deployment making it ready for staging. Lastly but significantly is the fact that your requirement calls for clear API documentation that ensures future iterations remain frictionless. My commitment is not just towards "now" but also a successful future iteration of the project where enhancements or maintenance might be required. This means that I provide concise documentation and a hand-off note that speaks volumes about each aspect of implementation so that it's impersonal irrespective of the changing hands. Choose me, Kai for this role & receive skillful implementation, smooth integration, 100+% dedication, and a pri Thanks!

@nileshp801

➡️ AI Agent Evaluation Platform Backend – FastAPI + PostgreSQL + Redis For multi step agent evaluation, do you need synchronous request response or async webhook based completion for longer running reasoning chains? ✅ My Blueprint ✔ Core Endpoints ➡️ POST /agent/submit – accept agent run data, return evaluation ID ➡️ GET /evaluation/{id} – fetch status, reasoning analysis, tool usage score ➡️ GET /aggregate – summarised scoring across multiple evaluations ➡️ POST /safety/flag – report safety violations with context ✔ Database & Cache ➡️ PostgreSQL for persistent evaluation records and scores ➡️ Redis for job queue management and temporary result caching ✔ Async Processing Ready ➡️ Background task queue for long running evaluations (Celery or FastAPI BackgroundTasks) ➡️ Webhook callback support when evaluation completes ✔ Testing & CI ➡️ Unit tests with pytest + mock ➡️ Integration tests for API endpoints ➡️ GitHub Actions workflow for automated test runs ✔ Deployment & Docs ➡️ Dockerfile + docker compose with API, PostgreSQL, Redis ➡️ Auto generated Swagger/OpenAPI docs (FastAPI native) ➡️ Brief handover note for future iterations ✅ Solution Framework ➲ Python + FastAPI ➲ PostgreSQL + SQLAlchemy or asyncpg ➲ Redis + Celery (if async needed) ➲ Docker Compose + GitHub Actions Ready to design your evaluation API layer. Nilesh P.

@Jasim0021

Hi there! ? I've carefully reviewed your API/backend system requirements and I'm confident I can deliver exactly what you need. **Why I'm the right fit:** • Designed and built 100+ RESTful and GraphQL APIs • Expert in API architecture, authentication, rate limiting, and documentation • Experience with microservices, Docker, Kubernetes, and serverless **My approach to your project:** 1. **Discovery & Planning** — I'll start by understanding your exact requirements, target users, and design preferences 2. **Architecture & Design** — Clean, scalable architecture with modern best practices 3. **Development** — Iterative development with regular demos and feedback loops 4. **Testing & QA** — Comprehensive testing including unit tests, integration tests, and user acceptance testing 5. **Deployment & Support** — Full deployment assistance and post-launch support **What you'll get:** • Clean, well-documented, maintainable code • Responsive design that works on all devices • Secure implementation following industry best practices • Timely delivery with daily progress updates • 30-day free bug-fix support after delivery **Relevant skills:** software development I'd love to discuss your vision in detail. Let's connect and make this happen! Best regards

@shubhj13

Hey, I am backend engineer specialised in scalable systems which work arround applied AI. I have shipped MVP for Indian startups before. Will be glad to work with you on this multi step evals system

@DreamDevil07

Hello, I am highly interested in working on your AI project. I have experience in AI tools, machine learning, automation, and problem-solving. I can deliver high-quality work within the given timeline and ensure accurate, efficient, and reliable results according to your requirements. I would love the opportunity to discuss the project further and start working as soon as possible. Thank you.

@tejasvjaiswal

With 3+ years of freelancing experience in AI and web development, I specialize in building intelligent, scalable, and user-focused solutions. My expertise includes Agentic AI systems, Machine Learning, Django, Flask, API integration, automation, and full-stack web development. I focus on delivering clean, efficient, and production-ready work with strong communication and fast turnaround times. I understand project requirements quickly and turn ideas into practical solutions that are both technically strong and business-oriented. Whether it’s AI automation, intelligent assistants, ML integration, or custom web applications, I bring problem-solving skills, reliability, and real-world development experience to every project.

@ToufeeqX

“I HAVE DONE SOMETHING SIMILAR BEFORE!” I understand the need for a clean, professional, and seamless backend for your AI-driven platform. My expertise lies in designing and implementing REST APIs in Python, connecting them to databases like PostgreSQL and Redis, and ensuring smooth integration with existing dashboards like Next.js. You won’t find someone more aligned with what you’re looking for. While I’m new to Freelancer, my current priority is building strong reviews and long-term client relationships, so you’ll receive serious effort and high-quality work at a much lower rate. Come chat with me, worst case you get a free consultation :) Regards, Toufeeq

@Foraxis

⭐ I handled a similar project ⭐, Happy to show you what works before you commit. I architected a FastAPI-based REST backend for an AI platform with similar functionalities as described, streamlining evaluation pipelines and ensuring seamless data flow and security. Aligned with your project needs, I bring expertise in crafting performant and secure REST APIs, prioritizing scalability and user experience. Understanding the complexity of AI-driven evaluations, my focus lies on performance optimization, stringent security protocols, and delivering a polished user interface. Specializing in backend development, I guarantee a robust solution with a keen eye on performance, security, and user experience enhancement. Invite the client to chat, mention a free consultation say,''Worst case, you walk away with a free consultation and a clearer understanding of your project. '' Kind regards, Curtley

@juan99lizano

You need clean FastAPI endpoints wiring agent evaluation pipelines to PostgreSQL and Redis, with Docker-compose and CI-ready tests — that's exactly what I can deliver. Recently built a FastAPI microservice for an ML inference platform: async endpoints, Redis job queuing, PostgreSQL result storage, auto-generated OpenAPI docs, and GitHub Actions CI — deployed via Docker-compose in under a week. Latency dropped 40% vs the previous Flask monolith. I'll deliver: all four endpoint groups (submission, status, scoring, safety flagging), unit + integration tests passing in GitHub Actions, a Docker-compose spinning up API/Postgres/Redis, and full OpenAPI docs with a handoff note — in 5–7 days. Do the evaluation pipelines already exist as Python functions/classes I'd wrap, or is the scoring logic also part of the scope to build?

@gonzadevops

I have extensive experience building scalable REST APIs with FastAPI, PostgreSQL, Redis, Docker, and async processing. For your AI evaluation platform, I will design endpoints for agent submission, evaluation status, aggregated scoring, and safety flagging using Pydantic schemas for validation, SQLAlchemy for database models, Redis for caching or job queues (with Celery or background tasks), and dependency injection for auth. Each endpoint will include automatic OpenAPI/Swagger docs, error handling, and logging. I will also write unit/integration tests with pytest, configure GitHub Actions CI, and provide a docker-compose file that spins up the full stack ready for staging. To give you a realistic timeline and budget, I need to understand the volume of agent evaluations expected, whether you need async webhook callbacks, and specific safety criteria. This is a reference price. My rate is $25 USD per hour. Let's schedule a brief call to review your existing code structure and evaluation pipeline. Best regards, Victor