Senior Backend Engineer for AI Finance Platform

@gangradeankit

With my extensive experience in building AI-powered SaaS platforms and MVPs using Python (including FastAPI) alongside Amazon Web Services, I am confident that I am more than qualified for your project. I have a track record of delivering robust, scalable backend systems that align perfectly with your requirements - including building efficient RESTful APIs, rate limiting, and background job processing. Additionally, my proficiency in CI/CD using Jenkins will ensure a well-automated, smooth deployment workflow. In terms of relevant projects, I developed a financial data analytics platform using multi-agent workflows and efficient document retrieval. This project involved creating a task orchestration system and production-grade APIs - experiences which align exactly with your needs for an AI Agents platform for financial research. Moreover, through these projects, I've built deep understanding of agent systems like RAG and tool calling including the necessary architectural considerations or queues, retries and monitoring. Additionally, robust logging mechanisms would be put in place to provide transparent visibility into the overall health for easy tracing of issues. Considering the long-term prospects of this project alongside my acclaim expertise in Backend development(flutter) & Amazon AWS ; let's converge our paths for a fruitful collaboration

@tobiask5

Hi there, I hope you are doing well I am highly experienced in building and scaling production backends with Python and FastAPI, including auth, rate limiting, background jobs, and high-traffic APIs. I have designed and deployed CI/CD pipelines with Jenkins, handling automated deployments, rollbacks, and monitoring. My architecture experience covers queues, caching, Postgres, Redis, and Dockerized services on AWS/GCP, ensuring reliability and cost efficiency. For agent execution, I would implement a queue-based system with prioritized tasks, retries with exponential backoff, observability via metrics/logging, and modular orchestration to scale multi-agent workflows. I am available immediately, working full-time in [your timezone], and can share relevant project examples demonstrating FastAPI and Jenkins deployments under load. Best regards, Tobias

@vishal1145

Your agent execution system will fail under concurrent load if you're running LLM calls synchronously without a queue layer. When 50 users trigger research workflows simultaneously, you'll hit OpenAI rate limits and PostgreSQL connection exhaustion within minutes. Before architecting the solution, I need clarity on two things: What's your current agent execution time (average and p99 latency)? And are you handling streaming responses from LLMs or batch processing only? These determine whether we need Redis pub/sub or a heavier message broker like RabbitMQ. Here's the architectural approach: - FASTAPI + CELERY: Offload agent tasks to async workers with priority queues so research jobs don't block API responses. Add rate limiting per user to prevent abuse. - JENKINS + DOCKER: Build multi-stage pipelines with automated rollback triggers if health checks fail post-deployment. I'll set up blue-green deployments to avoid downtime during releases. - POSTGRESQL OPTIMIZATION: Partition agent execution logs by date and implement connection pooling to handle 500+ concurrent queries without degrading performance. - RAG PIPELINE: Use pgvector for embeddings storage with HNSW indexing to keep retrieval under 100ms at 10K document scale. Add Redis caching for frequently accessed research results. - OBSERVABILITY: Instrument Prometheus metrics for queue depth, LLM token usage, and API latency. Set up alerting for when agent failure rates exceed 2%. I've built similar systems for 2 fintech clients - one scaled from 200 to 15K daily agent executions without infrastructure rewrites. I don't take on projects where the failure modes aren't mapped upfront. Let's schedule a 20-minute technical call to walk through your current bottlenecks and edge cases before committing to the build.

@NaveenSingh09

Hello, Senior Backend Engineer for AI Finance Platform I understand you need a seasoned backend engineer to scale a fastAPI‑based AI agent platform. My approach is three‑phase: 1) Design a stateless microservice layer with token‑based auth, rate limiting, and async background workers in FastAPI; 2) Build a robust Jenkins pipeline that provisions Docker containers, runs unit and integration tests, and deploys to AWS ECS with blue/green rollbacks; 3) Optimize latency by sharding PostgreSQL, adding Redis caching for frequent queries, and employing RabbitMQ queues for agent orchestration with retry logic and dead‑letter handling. I have shipped production FastAPI APIs handling 10k+ requests per minute, and created CI/CD pipelines that cut release cycles from days to hours. I’m available 40 hrs/week in IST. I’ll start immediately and keep the team informed through daily stand‑ups and Slack updates. Let’s discuss how we can accelerate your platform. Best Regards Naveen Thakur

@Ussama91

I'll build and scale core backend services for agent execution and research pipelines, leveraging Python, FastAPI, and Jenkins to ensure scalable architecture, reliability, and efficiency, with experience in LLM/agent systems and cloud platforms like AWS/GCP. I'll provide 2-3 relevant backend projects, FastAPI production experience, and Jenkins pipeline experience, along with timezone, weekly availability, and a note on architecting an agent execution system. Waiting for your response in chat! Best Regards.

@Shrutibimpl

Hello, 1) Which specific LLM providers or self-hosted models are you planning to use for the agent logic? 2) What is the average size and format of the documents being ingested for retrieval? 3) Do you prefer a specific message broker like RabbitMQ or should I use Redis for the background jobs? I will build your financial research platform using Python and FastAPI, focusing on a modular design that supports complex multi-agent workflows. I will implement a robust RAG pipeline to handle document retrieval and integrate tool-calling capabilities for the agents. To ensure the system scales, I will use a reliable message queue for background orchestration and manage persistent data in Postgres. For the infrastructure, I will set up Jenkins CI/CD pipelines to automate your Docker-based deployments and rollbacks on your chosen cloud platform. I will also incorporate Redis for efficient caching and session management, ensuring low latency and high reliability throughout the agent execution cycles. I can share you the details of the related project once the chat is initiated. Thanks, Bharat

@miscman

With my 13 years of extensive experience in the IT industry and a skill set that perfectly aligns with your project's requirements, I believe I can be a valuable asset to your AI Finance Platform. My proficiency in Python, FastAPI, Jenkins, as well as AWS, Docker, Postgres, and Redis equip me with the necessary expertise to deliver the scalable backend architecture your platform needs. Having worked on several production-grade APIs and implemented CI/CD using Jenkins for high-load environments, I have an in-depth understanding of their complexities and optimal management. Moreover, my previous work has involved building agent workflows, designing processes for efficient task orchestration and utilizing queues and caching for scalability. This directly relates to architecting an agent execution system which is a key responsibility outlined in your project description. I prioritize clean code combined with thorough testing and documentation, ensuring transparency and maintainability of the systems I build. My availability is immediate with a timezone of (mention timezone). Let's discuss further how we can maximize your system's performance, improve its latency and cost efficiency while maintaining high reliability and observability. I am confident about delivering only the best for your project.

@ritikgarg55

Hi there, my name is Ritik Garg and I'm a Full-Stack/Backend Developer from India. I have over 5 years of experience working with Python, Django, Restful APIs, Data Mining, Flask, Scrapy, Selenium, Flask, Node JS, Angular, React, AWS, DevOps, Azure, request, threading and selenium. My ultimate goal is to provide my clients with more than 100% satisfaction by implementing their ideas and helping their business thrive and grow. I always make sure to understand my clients' needs accurately and recommend the best approach for their project. I am always ready to take on any project and deliver it to the best of my abilities. Here are my core skills: - VueJS | JavaScript | Angular | ReactJs - NodeJS | Python - Bootstrap | CSS | CSS3 | HTML | HTML5 - Django | Flask - MySQL | MongoDB | SQLite | Postgres SQL - AWS, EC2, S3 DevOps Deployment

@amaank66

You’re building infrastructure, not experiments — so the backend must be resilient, observable, and scalable from day one. I’ve worked on production API systems and AI-integrated pipelines where reliability and latency mattered. 1) Relevant Backend Projects • AI-driven research aggregation platform (RAG pipelines + async task processing + FastAPI APIs) • Real-time analytics system (Postgres + Redis + background workers, high read concurrency) • SaaS automation engine (multi-tenant APIs, rate limiting, CI/CD with staged rollouts) 2) FastAPI Production Experience Built and deployed production FastAPI services handling high-concurrency workloads with: • JWT/OAuth auth • Background tasks (Celery/Redis) • Rate limiting + middleware logging • Structured OpenAPI docs • Containerized deployments (Docker) 3) Jenkins Experience • Multi-stage pipelines (build → test → lint → Docker build → deploy) • Branch-based environment deployment • Rollback pipelines • Secret management & artifact versioning 4) Timezone & Availability IST (UTC+5:30), flexible overlap with US/EU 30–40 hours/week available 5) Agent Execution Architecture (High-Level) • API layer (FastAPI) → enqueue task • Task broker (Redis/RabbitMQ) • Worker pool for agent execution • Retry with exponential backoff • Structured state store (Postgres) • Caching layer (Redis) • Observability (structured logs + metrics) • Dead-letter queue for failed tasks Best regards, Amaan Khan L. (CUBEMOONS PVT.)

@mobasherreza10

Hi there, Hope you are well. I am a senior backend engineer with deep production experience in FastAPI, Jenkins, and scalable architectures for AI agent platforms. I have built and scaled financial research systems handling millions of API calls, designed robust CI/CD pipelines with blue-green deployments, and architected agent execution frameworks with queues, retries, and monitoring. I am available full-time across US/UK/EU time zones and ready to start immediately. I can share relevant project details and code samples upon request. Let's connect to discuss your platform and immediate priorities. Best regards, Mobasher Reza.

@Visharaa2024

Hello, Your project caught my attention because I’ve been working on AI-driven backend systems and document intelligence pipelines, and the architecture you’re building (multi-agent workflows + research pipelines) is exactly the type of system I enjoy designing and scaling. Recently, I developed a financial document intelligence backend where FastAPI services handled document ingestion, processing pipelines, and AI-based querying. The system used PostgreSQL for structured data, Redis for caching/queues, and Docker-based deployments, allowing asynchronous workloads to run reliably even with heavy processing. For CI/CD, I have built Jenkins pipelines that automate build, testing, containerization, and deployment, including rollback strategies and environment separation (dev/staging/production) to ensure stable releases. How I would approach your agent execution system: I would design a FastAPI orchestration layer that schedules agent tasks through a queue system (Celery + Redis). Each agent runs as an isolated worker to allow horizontal scaling. Task states, metadata, and workflow tracking would live in PostgreSQL, while Redis manages queues and caching. Retries would use exponential backoff, and monitoring would be handled through centralized logging and metrics dashboards for observability.

@Hemantt841

You’re looking to build and scale a production-grade AI Agents platform for financial research, focusing on FastAPI backend services, Jenkins CI/CD pipelines, and scalable architecture with queues and caching. I understand the importance of reliable multi-agent workflows, document ingestion, and task orchestration in your system. With over 15 years of experience and 200+ projects completed, I specialize in Python backend development, PostgreSQL database design, and AWS cloud infrastructure. My expertise includes building production FastAPI APIs with authentication, rate limiting, and background jobs, alongside setting up Jenkins pipelines for automated deployments, rollbacks, and monitoring workflows. For your platform, I would design a robust backend leveraging FastAPI to handle high-throughput agent execution, integrating Redis queues for task orchestration and retries, with PostgreSQL for durable state management. Jenkins pipelines will be configured to automate CI/CD, ensuring smooth deployments and rollback capabilities. I can improve latency and observability by adding caching layers and monitoring tools, aiming for a stable and efficient system ready within a couple of months. Let’s discuss how I can help bring your AI finance platform to production and scale it effectively.

@usamaansari786

Hi there, I will build and scale your AI Agents platform immediately while ensuring high reliability for financial research workflows. Since you need a senior engineer to handle multi-agent orchestration and document ingestion, I will focus on a production-grade backend that is both fast and cost-efficient. I’ll implement a high performance FastAPI backend with Jenkins driven CI/CD pipelines to ensure seamless scaling and automated deployments of your research pipelines. This includes setting up robust RAG workflows, Redis caching, and RabbitMQ queues to handle agent execution and tool calling with high observability. I prioritize writing clean, well tested code using Docker and Postgres to maintain low latency responses for your financial multi agent system. I provide full source code ownership and technical documentation to ensure your platform's long term growth remains stable and secure. I am ready to join your team today. Let’s discuss how we can scale this platform together. Best Regards, Usama F

@marcioazambujo

Hi, there. I am interested in your project. Because your project is my major, I believe I am a right person for your project. I have extensive experience building production-grade Python backends with FastAPI, including APIs with authentication, rate limiting, background jobs, and high concurrency. I have designed and maintained Jenkins CI/CD pipelines for deployments, rollbacks, and automated testing, ensuring scalable and reliable systems. I am also experienced with AWS, Docker, Postgres, Redis, and have worked on LLM/agent workflows with document ingestion and task orchestration. I can architect an agent execution system using queues, retries, monitoring, and observability best practices to maximize reliability and throughput. I hope to hear from you. Thank you.

@mahads31

As an experienced and forward-thinking CEO of the technology-driven company, Prime Code Tech, I bring a multi-faceted skillset focused on delivering smart and scalable solutions. I have successfully led numerous projects that demanded meticulous architecture with modern technology, such as your financial AI Agents Platform project. With extensive proficiency in Python, including FastAPI, PostgreSQL, and critical backend development, I confidently assure an exceptional contribution to scaling this mission-critical platform. My unparalleled prowess in CI/CD using Jenkins aligns perfectly with your requirements for building and maintaining it. I've developed robust pipelines that primarily focused on deployments and rollbacks while never compromising efficiency or reliability. Moreover, my proficiency with AWS/GCP, Docker, Postgres, Redis further ensures that all architecture decisions are made to maximize scalability and performance without the added burden on cost.

@mindmagic1

Hello, I am a Senior Backend Engineer with extensive experience in building scalable systems and deploying production-grade APIs. After reviewing your job description, I’m confident I can help scale your AI Agents platform. Below are the details you requested: Relevant Projects: AI-powered Research Pipeline: Built a scalable backend for an AI research platform that ingests documents, orchestrates tasks, and performs analytics in real-time. High-traffic API Platform: Developed and scaled FastAPI-based services handling 500,000+ requests per day with efficient background job processing and rate limiting. FastAPI Production Experience: I’ve designed and deployed FastAPI-based services at scale, managing high traffic (50k+ concurrent requests). I’ve optimized APIs for performance, added background task processing, and integrated advanced authentication. Jenkins Pipeline Experience: I’ve set up and maintained CI/CD pipelines using Jenkins, automating deployments, rollbacks, and version management for microservices. Implemented multi-stage pipelines with testing, build, and deployment phases integrated with cloud services (AWS/GCP). I look forward to discussing this further and am excited to contribute to your project. Let me know if you need more details! Best regards,

@donc38

Hi — this aligns closely with systems I’ve built. 1) Relevant Projects • Multi-tenant AI research API (FastAPI + Postgres + Redis) powering RAG pipelines with background task orchestration (Celery/Redis), handling ~1M+ requests/month. • Document ingestion + vector retrieval service (chunking, embeddings, tool-calling agents) deployed on AWS ECS with autoscaling + observability. • Financial data processing backend with strict rate limiting, audit logging, and async job pipelines. 2) FastAPI Experience 5+ years Python, 3+ years FastAPI in production. Built APIs with OAuth2/JWT auth, RBAC, background workers, async I/O, rate limiting, and structured logging. Deployed behind ALB/NGINX, handling sustained high concurrency. 3) Jenkins Designed multi-stage pipelines: lint → test → Docker build → security scan → deploy (blue/green) → rollback hooks. Managed environment-based configs and artifact versioning. 4) Timezone & Availability UTC+1. 30–40 hrs/week. Immediate start. 5) Agent Execution Architecture API layer (FastAPI) → task queue (Redis/Celery or Kafka) → worker pool per agent type. Each task idempotent with retry + exponential backoff. Vector store (Postgres/pgvector). Redis cache for embeddings + session state. Centralized logging (OpenTelemetry) + metrics (Prometheus). Dead-letter queue for failed jobs + alerting. Horizontal scaling via container orchestration. Happy to discuss further.

@targaryen007

Hello, I’m a senior backend engineer with strong experience building and scaling production AI platforms in finance and data-heavy domains. I’ve shipped real-world systems using Python and FastAPI, handling auth, rate limits, background jobs, async workloads, and high-reliability APIs. Relevant work includes an AI agents & RAG platform with multi-agent orchestration, document ingestion, vector search, retries, and monitoring; and a financial research/algo trading backend using FastAPI, Postgres, Redis, Docker, and AWS. I’ve built Jenkins CI/CD pipelines covering tests, Docker builds, environment-based deployments, rollbacks, and secrets management. I’m comfortable improving latency, reliability, observability, and cost efficiency. Architecture approach: FastAPI for control APIs, Redis/queue-based workers for agent execution, idempotent task design with retries, Postgres for state, Redis for caching, and centralized logging/metrics for monitoring and cost control. Timezone: IST (UTC+5:30) Availability: 30–40 hrs/week Start: Immediate Happy to discuss architecture and scaling approach on a quick call. Amit

@karthikresonite

Hello, Resonite Technologies has senior backend engineers who have built and scaled AI-driven platforms in fintech and research domains. 1) Relevant Projects • Multi-tenant AI research SaaS with RAG pipelines (FastAPI + Postgres + Redis, AWS ECS deployment) • Financial analytics platform processing real-time data streams (async workers, task queues, cost-optimized inference) • Workflow automation engine with background job orchestration and rate-limited APIs 2) FastAPI Production Experience We’ve deployed FastAPI services handling 50k–200k daily API calls with JWT auth, RBAC, rate limiting, background tasks (Celery/Redis), async endpoints, and structured observability (Prometheus + OpenTelemetry). 3) Jenkins Experience Built full CI/CD pipelines: • Lint → Test → Build Docker → Push → Deploy (staging/prod) • Blue/green deployments • Rollback automation • Environment-based config & secrets handling 4) Timezone & Availability IST (UTC+5:30), overlap-friendly with EU/US. 40+ hrs/week available. 5) Agent Execution Architecture (High-Level) • API layer (FastAPI) → enqueue tasks • Redis/Celery or Kafka-backed queue • Worker pool per agent type • Idempotent task design + retry with exponential backoff • Postgres for state tracking • Redis cache for retrieval speed • Observability: structured logs + metrics + tracing • Dead-letter queue for failed workflows Best regards, Resonite Technologies