Senior Backend Engineer — Agentic AI & Data Platform

@augurstech

A Warm Hello! We are readily available to start working on this project! We’re a senior engineering agency specializing in scalable backend systems, distributed architectures, data platforms, cloud infrastructure, and AI-powered applications. We would be happy to discuss your current architecture, expected data volumes, ingestion sources, AI/LLM requirements, and the first set of platform capabilities, and then propose a practical architecture and implementation roadmap. We’re confident we can contribute at the senior engineering level this project requires and help build a platform that is scalable, reliable, and ready for significantly more sophisticated agentic workflows over time. Best regards, Ana

@diektech

My name is Elias, and I am a Miami-based senior software engineer with 20 years of experience in backend development, particularly in building scalable systems. I reviewed your project for the Agentic AI & Data Platform and understand that your main goal is to design a robust backend capable of efficiently handling data processing and API integrations. I have built similar systems that emphasize secure architecture, scalability, and performance. My approach to your project would involve these phases: 1) Understand the business logic and technical requirements. 2) Design the database schema and API architecture. 3) Develop core backend features and microservices. 4) Conduct thorough testing for reliability. 5) Deploy the system and provide ongoing support. Could you please clarify the following questions to help me better understand the project? 1) What specific data sources will the platform integrate with? 2) Are there specific user roles and permissions that need defining? 3) What edge cases should I consider for data processing workflows? I have built similar systems before, though most were completed under NDA, so I can't share links. However, I’d be happy to discuss the architecture and features of those projects. Additionally, I will ensure a maintainable codebase that adapts to future needs.

@shrec

Hi there, I can contribute to this as a backend engineer focused on scalable APIs, asynchronous processing, data pipelines, and production reliability. The architecture you describe needs more than CRUD development, so I’d approach each feature from requirements and system design through implementation, testing, deployment, and operational support. I can work across Go, Java, Python, or TypeScript and build REST/gRPC services, microservice architectures, PostgreSQL/MySQL data layers, background workflows, and integrations with external services. I can also work on Docker/Kubernetes deployments and backend components involving LLMs, RAG, and AI-driven workflows. For your platform, I’d pay particular attention to service boundaries, event-driven processing, data consistency, observability, security, and low-latency retrieval. I’m happy to discuss specific architecture challenges and review the existing system before proposing the implementation approach. Best, Ivane

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Scalable Backend Systems for Your Agentic Data Platform ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a Senior Backend Software Engineer. You need not look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for backend systems. I will design and implement scalable services, build reliable APIs, and ensure seamless data processing while staying within your budget. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in building complex backend systems. My expertise includes designing scalable APIs, working with PostgreSQL and MySQL, and implementing microservices. I have a strong grip on other relevant technologies like Docker, Kubernetes, and cloud platforms, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I'm excited to explore how I can contribute to your success. ➡️ Skills & Experience: ✅ Golang or Java/Kotlin ✅ Python or TypeScript ✅ PostgreSQL / MySQL ✅ REST and gRPC APIs ✅ Microservices Architecture ✅ Kafka or Streaming Technologies ✅ Google Cloud Platform (GCP) ✅ AWS/Azure Expertise ✅ Docker and Kubernetes ✅ Data Processing Pipelines ✅ AI Integration ✅ System Performance Optimization Waiting for your response! Best Regards, Zohaib

@corpmember29

HELLO, I CAN HELP YOU BUILD A PRODUCTION-GRADE AGENTIC AI DATA PLATFORM — FROM SYSTEM DESIGN TO SCALABLE BACKEND SERVICES AND PRODUCTION OPERATIONS. I have carefully reviewed your requirements and understand that this is far beyond CRUD development. You need a senior engineer who can design distributed systems, build reliable data pipelines and integrate AI agents into a scalable backend architecture. I have 11+ years of software development experience across backend engineering, distributed systems, microservices, APIs, databases and AI-powered applications. I can work with Go/Java, Python, TypeScript, PostgreSQL/MySQL, REST/gRPC, Kafka, Docker, Kubernetes and AWS/GCP environments. I can design asynchronous and event-driven workflows for ingestion, enrichment and processing, build low-latency APIs and streaming services, and integrate LLMs, RAG and agentic workflows into backend pipelines. I’ll also focus on observability, security, fault tolerance, performance optimization and production reliability. My approach is requirements → architecture → implementation → testing → deployment → monitoring, with clear technical tradeoffs and maintainable code throughout. I CAN SHARE RELEVANT BACKEND/AI PROJECT EXPERIENCE AND EXPLAIN MY SPECIFIC CONTRIBUTION, TECHNOLOGIES AND ARCHITECTURAL DECISIONS. Let’s have a quick call to discuss your existing architecture and the first backend components you want to build. Thanks, Christina

@rohitpatidar001

Hello, I’m a Senior Java Backend/Full-Stack Developer with 11+ years of professional experience, and I’m comfortable taking ownership from requirements and system design through implementation, testing, deployment, and production troubleshooting. I have worked on enterprise backend systems involving: * Spring Boot microservices and REST APIs * PostgreSQL/MySQL database-backed business applications * Authentication and authorization * Third-party API integrations * Asynchronous/background processing * Webhooks and event-driven workflows * Financial transaction processing * Document/PDF processing * Production monitoring, debugging and security remediation One relevant example is a financial services API platform involving authentication, KYC, bank-transfer workflows, third-party integrations, idempotency, asynchronous processing and transaction handling. ### 2. Distributed systems & microservices My strongest experience is designing and developing modular/microservice-oriented Spring Boot backend systems. I focus on clear service boundaries, API contracts, database consistency, failure handling, idempotency, logging, security and maintainability. I’m comfortable analyzing an existing architecture and improving it rather than introducing unnecessary complexity.

@adeelpirzada

An agentic data platform lives or dies on the ingestion side, so I would start there: event-driven ingestion into Postgres with Kafka for streaming, enrichment as idempotent async workers, and retrieval exposed behind gRPC internally with REST at the edge. Embeddings and metadata separated so agents can query structure and semantics independently, all containerised on Kubernetes. My side of that is Python and Node.js, with .NET for enterprise services. On Boostifai I built an AI SEO platform doing content analysis and generation at scale, which is the same shape of problem: large corpora, LLM calls in the pipeline, and cost and latency budgets that force caching and batching. Code3Scribe covers the agentic narrative generation part. What does the existing system look like today, and which pipelines are in scope for migration first?

@efanntyo

Hi there, I'm excited about the opportunity to contribute as a Senior Backend Engineer for your Agentic Data Platform. With extensive experience in designing and building complex backend systems, I am well-equipped to tackle the challenges of creating scalable, AI-integrated infrastructure. I have a strong background in Java, Python, and Golang, and have successfully implemented microservices architectures using Docker and Kubernetes. My experience with REST and gRPC APIs ensures efficient communication between services, while my proficiency in PostgreSQL/MySQL and NoSQL databases guarantees robust data handling. In previous projects, I’ve integrated LLMs and AI agents to enhance data processing and discovery. I've also developed event-driven systems using Kafka, ensuring low-latency and reliable data pipelines. My hands-on experience with GCP and AWS strengthens my ability to deploy and manage services in production environments securely. I approach projects by translating requirements into comprehensive system designs, leading to seamless implementation and deployment. My focus on performance, scalability, and security aligns with your project needs. I thrive in environments where I can make impactful architecture decisions and work independently, yet collaboratively. I look forward to the possibility of discussing how I can contribute to the success of your platform. Best Regards,

@toriquldev123

Hello There! I’m Md Toriqul Islam, a full-stack engineer with 10+ years of experience designing scalable backend systems, APIs, data-driven platforms, and cloud-based applications. I understand you need a production-grade Agentic Data Platform supporting scalable ingestion, enrichment, asynchronous processing, event-driven workflows, intelligent retrieval, and LLM/AI-agent integration. I can contribute across architecture, implementation, testing, deployment, optimization, and production operations, with strong focus on reliability, security, and scalability. I am skilled in Python, Java, Node.js, REST APIs, PostgreSQL, MySQL, Docker, Kubernetes, AWS, microservices, and AI integrations. I have some questions: 1. Is there an existing backend architecture to extend or migrate? 2. Which streaming technology and cloud environment are currently preferred? 3. What LLM/RAG or agentic workflow components already exist? I’m ready to discuss the architecture and begin contributing independently. Best regards, Md Toriqul Islam

@tangramua

Hello,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Java, Python, Golang, NoSQL Couch & Mongo, Node.js, Docker, Backend Development, Database Design, API Development, Microservices and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@denisSolution

Hi, I'm Denis, a full-stack developer with extensive experience building distributed backend systems, data pipelines, and AI-integrated services. I've worked on large-scale platforms with microservices, streaming infrastructure, and LLM workflows—directly aligned with your needs. I understand your core challenge: building a scalable backend that ingests, enriches, and retrieves data while supporting AI agents and automated workflows. This requires careful design around data flow, APIs, async processing, and production reliability. I've built similar systems using microservices architectures, event-driven pipelines, and LLM-based enrichment. I'm proficient with PostgreSQL at scale, REST/gRPC APIs, Docker, Kubernetes, and cloud infrastructure (GCP/AWS). My approach starts with requirements review and system design to establish clear data flow, API contracts, and service boundaries. I'd build incrementally—reliable data ingestion first, then enrichment logic and AI agent integration, followed by discovery and retrieval. Throughout, I prioritize observability, testing, and production readiness. I'd focus on how data flows through your pipeline and how agents interact with it. The architecture must support both synchronous queries and long-running async jobs, handle retries gracefully, and maintain consistency across services. One key risk: unclear boundaries between agent ownership and platform responsibilities. Clarifying this early prevents costly redesign. I'm ready to

@vishal1145

Your LLM agent orchestration layer will fail under concurrent load if you're planning synchronous API calls to external models without implementing a proper queue and retry mechanism. This creates cascading failures when OpenAI or Anthropic hit rate limits during peak ingestion. Quick questions - are you planning to run agent workflows in-process or as isolated services with their own resource pools? And what's your target p99 latency for the data enrichment pipeline when processing 10K+ documents per hour? Here's the architectural approach: - GOLANG MICROSERVICES: Build event-driven services using gRPC for inter-service communication and Kafka for async agent task distribution, ensuring each LLM call runs in isolated workers with circuit breakers. - POSTGRESQL OPTIMIZATION: Design partitioned schemas for multi-modal data with JSONB indexing for vector embeddings and implement connection pooling to handle 500+ concurrent enrichment jobs without lock contention. - GCP INFRASTRUCTURE: Deploy on GKE with horizontal pod autoscaling, Cloud Pub/Sub for streaming ingestion, and Cloud Run for stateless agent executors that scale to zero between workflows. I've built similar agentic platforms for 2 enterprise clients processing 50M+ records monthly with sub-200ms API response times. Let's schedule a technical call to walk through your data schema and agent coordination strategy before implementation starts.

@amirmujtaba92786

Hi, I am an Experienced Full Stack Developer. I can provide services related to AI: LangChain, LLM, RAG, Agent, Tool Chatbot, Voicebot OpenAI, Gemini, HuggingFace Python, FastAPI, Flask Frontend: Angular, Angular Material Streamlit React JS HTML, CSS, Bootstrap Graphs/Charts Tailwind CSS Theme/Template integration Google maps Backend: .NET Core, C#, Web API, Entity Framework, ABP Framework Java, Spring boot, Hibernate, JPA, Maven/Gradle Node JS REST API development/integration 3rd party integration Mobile App: Flutter, Dart, Firebase, Firestore Database: MSSQL Server, MySQL, PostgreSQL, Oracle, mongodb, Sql/Nosql Other: AWS (EC2, Elastic Beanstalk, RDS, S3, SES, SNS) Payment Gateway Integration Docker etc. I would like to have a chat or call to talk in details about project, timeline and budget etc., thank you. Best regards Amir

@gutibs

Hi! This is something we can handle end-to-end — and the agentic + streaming combination is genuinely interesting territory to work in. Before getting into specifics, the piece that changes the approach most is the migration scope. You mention migrating existing services and pipelines with minimal downtime — is the bulk of this engagement greenfield, or primarily rearchitecting a running system? That distinction drives how we structure the early phases and how aggressively we can move on the new infrastructure without disrupting what's live. On how we'd build it: for the high-throughput ingestion and streaming layer, Golang is our preference — tight concurrency control, predictable latency under load. Python handles the LLM integration and agentic orchestration cleanly, and the two sit naturally behind a gRPC service boundary. For the AI layer, we'd build RAG over the actual data corpus — pgvector or a purpose-built vector store depending on query patterns and data modality — with agent workflows structured as explicit, observable pipelines rather than free-running loops. That makes debugging in production tractable, which matters once you're operating at scale. Kafka for event streaming, Kubernetes for orchestration, GCP-native services where they reduce operational surface. On architecture and tradeoffs: comfortable owning those decisions end-to-end, from design through production operations. Gustavo & the DoTheCode team

@PINESCRIPTERR

Hi, I can contribute at the architecture and implementation level across the complete lifecycle of this agentic data platform. My preferred core stack is Go for high-throughput services, Python for LLM and enrichment workers, PostgreSQL, Kafka, gRPC/REST, Kubernetes, and GCP-managed infrastructure. I would structure the platform around durable ingestion, schema validation, event-driven processing, idempotent enrichment, lineage tracking, and independently scalable workers. Agent and RAG services would include hybrid retrieval, metadata filtering, evaluation traces, prompt/model versioning, permission-aware context, and controlled tool execution. Production readiness would cover contract and integration tests, observability with OpenTelemetry, dead-letter handling, replayable events, backpressure, workload isolation, secrets management, and zero/minimal-downtime migration strategies. I’m comfortable owning technical design, implementation, code review, deployment, incident diagnosis, and performance optimisation. Relevant examples of distributed backend, streaming, and LLM-enabled systems—including technologies and specific engineering contributions—can be discussed privately. Best regards, Houssame

@emilesd617

Hi, I am a backend developer with 8 years of rich experience in software development, with a background in scalable backend systems, API development, data processing, microservices, and AI enabled applications. I am familiar with Python, Java, Node.js, Golang, PostgreSQL, MySQL, MongoDB, REST APIs, gRPC, Docker, Kubernetes, microservices, cloud platforms, LLM integrations, and asynchronous processing. For your agentic data platform, I can design modular backend services around reliable ingestion pipelines, asynchronous workflows, event driven processing, and scalable APIs. I can structure the data layer for large datasets and integrate LLM and agent workflows for enrichment, discovery, and intelligent retrieval, with appropriate caching, queues, monitoring, and security. I can also approach existing service migrations carefully to minimize downtime and take features through system design, implementation, testing, deployment, and production support. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@komalshaikh92

Hello, The key part of this platform is not simply building another set of backend APIs, it’s designing the distributed architecture so ingestion, enrichment, agent workflows and data discovery remain reliable and scalable as dataset size and workload grow. I’d approach this by first understanding the existing architecture and data flows, then defining clear service boundaries, asynchronous processing patterns and reliable event contracts. I can work across Go/Java/Kotlin with Python/TypeScript where appropriate, PostgreSQL/MySQL, Kafka-style streaming, Docker/Kubernetes and cloud infrastructure. For AI workloads, I’d structure ingestion, enrichment, retrieval and agent execution as observable, fault-tolerant backend workflows with appropriate security and performance controls. 1. Is there an existing backend architecture/codebase I would be taking over, or is the platform being designed from the ground up? 2. Which area is the immediate priority: data ingestion/streaming, core backend services, or the agentic AI layer? Some technical details are difficult to cover here due to Freelancer.com’s character limit. If you need someone who can reason through the architecture rather than simply implement assigned tickets, please open a chat and let’s discuss the existing system and first backend priorities.

@shivmirthyusl

Hi, As an experienced backend engineer, I am confident that I have the skills required to tackle your Agentic AI & Data Platform project head-on. Over my 19+ years in the industry, I have successfully built and scaled complex backend systems exactly like the one you are seeking. Working with Golang and Java, I have designed microservices and API's while integrating databases like PostgreSQL and MySQL. My experience extends beyond simply building systems to diving deep into existing ones, which aligns perfectly with your need for someone who can make sound technical decisions even when faced with an unknown system. My understanding of distributed systems, microservices, and significant time spent on AWS and GCP provide me with a solid foundation for streamlining data pipelines efficiently and securely. Moreover, my proficiency in Python complements my expertise in LLMs, AI agents, RAG systems, which is invaluable for building intelligent data enrichment and discovery services as required by your project. Thanks, SBM

@HamparProd

The part that interests me most here is building the data and workflow layer beneath the agents, where ingestion, retries, provenance, retrieval and production reliability have to remain deterministic even when the LLM layer is not. My backend stack includes Go, Python, TypeScript, PostgreSQL, AWS, Docker/Kubernetes, APIs and LLM/RAG integrations. I’m comfortable owning work from system design through implementation, testing, deployment and production debugging. For distributed systems I focus on idempotent consumers, explicit event contracts, retries/DLQs, backpressure and observable service boundaries. For streaming/data pipelines I separate ingestion, normalization, enrichment and indexing so stages can scale and replay independently. For AI systems I have worked with LLM/RAG and agent-style backend workflows, including retrieval, external tools/APIs and structured outputs rather than relying on unconstrained model responses. Cloud: AWS production experience; architecture can map cleanly to GCP. Preferred core: Go + Python, PostgreSQL, REST/gRPC, event streaming, Docker/Kubernetes. Rate: $38/hour. I can start immediately after award.