Senior Backend/AI Platform Engineer Needed

@augurstech

A Warm Hello! We are readily available to start working on this project! We are a senior full-stack, backend, AI platform, and cloud engineering agency with experience taking existing SaaS and AI-powered applications from prototype/early production architecture to secure, maintainable, enterprise-ready platforms. Our objective is to leave the platform with a clear service boundary, secure multi-tenant architecture, isolated AI capabilities, enterprise-ready authentication/authorisation, reliable deployment infrastructure, and sufficient automated testing to support continued development. We would be happy to begin with the codebase review and then provide the recommended architecture, confirmed scope, engineering estimate, milestones and fixed-price option before proceeding with the larger implementation. Best regards, Ana

@irsolutions

With over a decade of experience in full-stack architecture and high-scale systems, I understand the importance of restructuring and hardening platform architecture for enterprise deployment. Your project goal of improving separation between the application, backend, and AI services aligns perfectly with my background in scaling systems for over 1 million users and developing high-security FinTech solutions. I recommend implementing a microservices architecture to ensure scalability and security. This approach will separate the AI agents from the main application while creating secure APIs for communication. My past success in building Telegram Mini Apps for a large user base showcases my ability to handle complex systems like the one you are aiming for. I encourage you to reach out so we can discuss your project roadmap in more detail. Together, we can ensure the successful restructuring and hardening of your platform architecture to meet the needs of your enterprise deployment.

@adeelpirzada

Hi, I am Adeel. I built Boostifai, a multi-tenant AI SaaS with subscription tiers, where separating the core app from the AI processing layer was a constant design concern, so this kind of restructuring is familiar territory. I would start by drawing a hard boundary around the AI agents first, moving them behind an authenticated service API with its own timeouts and validation, then layer RBAC and org-level isolation on top once that boundary is solid. Security hardening and regression testing would run alongside each phase rather than at the end, so nothing breaks silently as the refactor lands. Is the current AI agent code already isolated into separate modules, or still calling straight into the main application?

@flapjakc

Hi, Mateo here, from Toronto. Separating AI agents from the main application only actually improves security if the boundary sits at the network/service level, not just a different folder in the same codebase, since the real risk you're describing, sensitive AI operations reachable from the frontend, only goes away once agent calls physically can't happen without passing through an authenticated service layer first. Given the 32-59 day estimate spans architecture review, RBAC, deployment, and security hardening, I'd start with the codebase review exactly as you've outlined, producing the recommended architecture, confirmed scope, and a fixed-price quote for the full build before committing days to any specific number, since a multi-tenant SaaS refactor like this genuinely shouldn't be priced blind. Given the real breadth here, onboarding boundary, AI separation, RBAC, security hardening, deployment architecture, would starting with the codebase review and architecture proposal as Phase 1 work for you, before locking in the fixed price for the full refactor? Looking forward to working with you.

@Herculesz

Hi, This project requires careful architectural refactoring to securely separate AI services from the main backend while enforcing robust multi-tenant authentication and RBAC. The main engineering risk lies in ensuring secure, scalable service boundaries and protecting sensitive operations from unauthorized access. I usually structure systems to separate onboarding, AI processing, and core business logic into distinct services with well-defined APIs and strict authentication layers. My experience includes reviewing legacy codebases to identify coupling and security gaps, then implementing scalable, maintainable backend architectures. In a prior AI-driven marketing platform, I integrated AI modules with backend services to enable modular AI workflows, which aligns with your need to decouple AI agents. I approach authentication by designing session and token management that supports enterprise SSO and multi-tenant isolation. I recommend separating AI agent APIs behind secure service gateways with validation, timeout, and error handling. Logging and monitoring will be critical for security and operational insight. I can help outline the refactor strategy and define secure API communication patterns to prepare your platform for enterprise deployment. Thanks, Hercules

@longngoc2307

Perfect Match "SECURE MULTI-TENANT AI BACKEND ARCHITECTURE" — you need the existing platform hardened and separated for enterprise use. I’d start with the codebase and trace auth, tenant context, API boundaries, AI calls, and database access before changing structure. The key decision is to keep AI agents behind authenticated backend services, so frontend users cannot call sensitive operations directly. I’d also test tenant isolation and RBAC at the API and database-access layers, not just in the UI. That catches the dangerous case where a user changes an ID in a request and reaches another organisation’s data. Which part is currently causing the most risk: authentication/RBAC, AI service separation, or the existing backend structure? Looking forward to work with you.

@melikatfz

Hello, The key architectural challenge here is separating application responsibilities from AI execution without weakening authentication, tenant isolation or service-to-service security. I’d approach the existing codebase by first mapping the current request, authentication and data-access flows, then tightening the application boundary, RBAC and organisation context before extracting AI workloads into independently secured backend services. The resulting structure should give each service clear responsibilities, validated APIs, controlled timeouts, protected credentials and consistent error handling, while keeping future agents and enterprise SSO straightforward to introduce. I’d also review database access patterns, tenant isolation, protected routes, session handling and the AWS/Vercel deployment model, followed by integration and regression testing. The main point to establish early is how much of the existing AI/backend architecture can be retained versus requiring deeper refactoring. Have a nice day

@Giovanni704

Hi there! As I have read, I see you need help restructuring and securing an existing AI SaaS platform with backend architecture, authentication, RBAC, APIs, and AI services, and I could help you! I'm a professional Software Engineer with more than 10 years of experience and more than 350 projects done here on this platform. I offer unlimited revisions and I don't require any payment in front. I always meet delivery deadlines working until I get the desired results. Send me a message so I can show you my previous work and we can discuss more. Best Regards, Giovanni.

@Eliteinno

✋ Hi, the central risk here is cross-layer trust: if onboarding, frontend, backend, AI agents, and data access do not share one clear identity and permission model, tenant isolation and enterprise security become fragile very quickly. I’d start by tracing the current request/auth flow end to end, then define organisation context, RBAC, protected service boundaries, and the contracts between the core backend and AI services. From there I’d refactor incrementally behind tests, keeping AI operations server-side, enforcing tenant-aware data access, and hardening sessions, admin controls, database access, and deployment boundaries without destabilising the existing product. What is the current backend/AI stack and how is tenant context enforced today across API requests and database queries?

@XRProConnect

Hi, Aashiq (Ash) here from Cape Town, South Africa. This project instantly caught my eye, so I had to reach out. I see you’re looking for someone to strengthen your AI-powered SaaS platform’s backend architecture, especially focusing on securing user access and decoupling AI agents. With extensive experience in building scalable SaaS platforms, I have helped businesses enhance security and performance using technologies like Node.js, AWS, and PostgreSQL. I’m confident I can deliver the robust architecture you need, and I’d love to share examples of my past work. Based on what you mentioned, here is how we would approach the project: - Assess the existing architecture and identify weaknesses. - Restructure the onboarding and application boundaries. - Implement secure API communications between services. - Enhance user authentication and RBAC for multi-tenancy. You can expect clear communication throughout the project, with a focus on delivering a user-friendly solution optimized for performance. Best Regards, Aashiq

@SkillCrafts

I've successfully architected and implemented similar platform hardening initiatives, specifically focusing on enhancing microservice isolation and bolstering enterprise-grade security for AI-driven applications. My experience with [mention a specific technology or concept from the job description, e.g., isolating inference endpoints] aligns directly with your need to improve separation between application, backend, and AI services. My approach will involve a deep dive into your existing codebase and infrastructure. I'll utilize tools like Docker and Kubernetes for containerization and orchestration to enforce service boundaries. For authentication and authorization, I'll leverage industry-standard solutions such as OAuth2/OpenID Connect and implement role-based access control (RBAC) policies. Security hardening will include vulnerability scanning, secure coding practices review, and potentially implementing an API Gateway for centralized security enforcement. A phased rollout with robust CI/CD pipelines and automated testing will ensure stability and minimize disruption. To ensure optimal alignment, could you elaborate on your current authentication and authorization mechanisms? Additionally, what are your primary concerns regarding the existing deployment architecture and its scalability for enterprise use? I'm eager to discuss how my expertise can directly address these challenges and contribute to a more robust and secure platform.

@dvcontact

Hello Sir, Are you open to a no-commitment demo showcasing an optimized architecture for your AI-powered SaaS platform? With extensive experience in SaaS and AI integrations, I can provide immediate insights into enhancing your platform's security and scalability. Let's connect to discuss how I can add value to your project and elevate your backend architecture. Best, Smith

@rohitpatidar001

Hello, I’m a Senior Backend / Full Stack Developer with 11+ years of experience building and refactoring enterprise applications, microservices, secure REST APIs, database-driven platforms, and production systems. Your project is particularly interesting because the first stage focuses on reviewing an existing codebase and strengthening the architecture before enterprise deployment. This aligns well with my experience working with existing systems, identifying architectural issues, improving service boundaries, securing APIs, and implementing changes without unnecessarily disrupting existing functionality. My strongest areas relevant to this project include: * Backend and REST API architecture * Authentication, authorization and JWT-based security * RBAC, protected APIs and role-based access control * Multi-tenant application and data-isolation design * PostgreSQL and relational database architecture * Service-to-service API communication * Input validation, error handling and security hardening * Microservices and separation of business/data-access layers * Docker, Git/GitHub and CI/CD * Integration, regression and API testing * Production debugging, performance and reliability improvements * React and frontend/backend integration * Integration with AI/LLM APIs and backend AI workflows

@sekai1116

I can help restructure and harden your existing AI SaaS platform for enterprise deployment, working directly with the current codebase rather than treating this as a greenfield rebuild. My first step would be a focused architecture and security review covering the onboarding/application boundary, authentication, RBAC, tenant isolation, database access, API boundaries, and the existing AI/LLM architecture. From there, I can separate AI agents into properly secured backend services with authenticated service-to-service APIs, validation, timeouts, structured contracts, and controlled access to sensitive operations. I can also refactor the backend into clearer application, business-logic, AI-service, and data-access layers while strengthening sessions, roles, organisations, admin controls, logging, secrets management, rate limiting, and error handling. For deployment, I can review the AWS/Vercel setup and recommend an architecture appropriate for the existing workload and future enterprise integrations. I’ll also include authentication/RBAC testing, regression testing, technical documentation, and clean commits to the existing repository. I’m available to begin with the architecture review and provide the target architecture, risks, milestones, and engineering estimate afterward.

@aprkdream

Hi, Your core challenge is not adding features, but tightening the seams between your application, backend, and AI services to prevent security debt from compounding. When authentication leaks or service boundaries blur, enterprise deployment becomes risky. I understand the frustration of maintaining a system where permission models are inconsistent, so I prioritize auditability and stable integration flows. With 11 years of full-stack experience, I have managed multi-tenant ERP and RBAC systems like AERO, where enforcing strict permission boundaries and secure API interactions was critical to maintaining data integrity across complex roles. My approach ensures that your refactor strengthens the platform without breaking existing workflows. - Audit current onboarding-to-application boundaries to identify where auth checks are bypassed. - Implement strict OAuth flows and role-based access controls to secure data access. - Isolate AI logic from core backend operations using clean REST API contracts. - Introduce comprehensive logging and retry mechanisms for resilient API interactions. - Harden the PostgreSQL environment with security controls and consistency checks. - Establish a testing suite for critical auth paths to validate the new architecture. The primary risk is breaking existing workflows during the refactor. I will start by auditing your current onboarding flow and OAuth implementation to create a safe migration plan. Do you have a current runbook for your existing permission model, or should I derive it from the codebase? Best Regards, Anil Prajapati

@shubhi8982

As a highly experienced Senior Backend / AI Platform Engineer, focussed on architecture and security, I am excited to bring my skill set to your project. My broad skill range in Python, Node.js, PHP/Laravel, Java,.Net and Javascript will prove invaluable when it comes to the task at hand: refactoring your platform to enhance the separation between application and backend layers. Additionally, my proficiency in database management, secure API implementation and AWS/Vercel deployment architecture will ensure that your data and processes are protected while maintaining their accessibility. While your project is not greenfield, it requires an engineer who can absorb existing codebases and platforms quickly; another area where I excel. I have a proven track record of stepping into existing projects, quickly understanding the structure and developing efficient and maintainable solutions to meet new requirements and improve security. I'm also comfortable recommending which services should be hosted where in both Vercel and AWS. Given my familiarity with building SaaS platforms & CRM systems with multi-user access & document/data permissions, I am equipped to address all the various requirements mentioned in the job description of user authentication & sessions (SAA)

@rajeshrolen

Hi, At first glance, this looks straightforward but there’s usually one part that causes issues later. I’ve handled similar work before and can help you avoid that. Regards, Rajesh

@TechWagger2026

As a seasoned Backend and AI Platform engineer with over 14 years of experience, I bring the specialized skills and expertise needed to excel in your ongoing project. Having worked on diverse platforms spanning the Online Delivery, Medical, Real Estate, Shipping, and Education industries, I have successfully delivered over 416 projects with utmost client satisfaction - with a proven track record. An average day for me revolves around developing innovative solutions for complex business workflows precisely what your project requires. From onboarding & application boundary restructuring to backend refactoring implementing robust authentication mechanisms/RBACs and AWS / Vercel Deployment ArchitectureߞI've handled it all! Rest assured that my solutions are driven by enterprise-grade security standards interwoven with scalability-focused designs. With my expertise in AI and LLM, albeit basic, I am capable of understanding and decoupling the AI agents from your main platform, ensuring future-proofed scalability for new AI agents. My overall commitment lies not in just delivering technically sound solutions but also integrating User-friendly elements that result in improved user experiences. The fact that I dedicate myself to holistically understand your unique business requirements allows me to deliver bespoke solutions suited exclusively for your use-case.

@RaqiOfficial

Hello, I’d structure the refactor around a trusted identity layer, organisation/tenant context, RBAC, core business services, isolated AI services, controlled data-access boundaries, and deployment-level separation. The key architectural decision is making authorisation and tenant isolation enforceable at every backend boundary rather than relying on frontend state or scattered permission checks. I’d first document the existing architecture and trust boundaries, then introduce typed service contracts, service authentication, validation/timeouts, tenant-aware repositories, secure session handling, admin permissions, and an AI-agent interface that supports additional agents without coupling them back into the application. Refactoring would be staged behind integration/security tests so the existing codebase can evolve safely. Are the AI agents currently running inside the same Node/Next deployment as the application backend, or are any already deployed as separate services? Share the current architecture diagram or repo structure and I can identify the safest first boundary to extract. Thanks