AI Integration Engineer / Backend Developer

We are looking for an intermediate Backend Developer specializing in NestJS who is passionate about practical AI engineering, data management, and automation. In this role, you will focus entirely on the application and data layer. You will be responsible for building the pipelines, API bridges, and retrieval systems that allow advanced language models to securely access workspace data, reason through workflows, and safely execute real-world tasks across multi-tenant systems.

You will work closely with our frontend team, providing the robust AI infrastructure and endpoints they need to build intelligent user interfaces across a diverse suite of proprietary agency products and client platforms.

• AI Capabilities Integration: Architect and maintain secure backend integrations with production-grade LLM APIs to fuel our application logic.
• RAG Pipeline Engineering: Design, build, and optimize high-fidelity Retrieval-Augmented Generation pipelines, managing text chunking, token tracking, and context window optimization.
• Vector Database Management: Manage semantic search infrastructure using vector databases and extensions to index, store, and query high-dimensional embeddings efficiently.
• Agentic Workflows and Tool Use: Design and implement autonomous agent behaviors utilizing function calling, background workers, and tool utilization to allow AI layers to safely modify database states, handle workflows, and automate tasks.
• Model Context Protocol Implementation: Set up and maintain MCP servers and clients to establish a standardized, highly secure data-sharing protocol between our underlying databases and LLMs.
• Secure Data Architecture: Enforce strict data perimeter boundaries, data isolation, and sanitization protocols so that AI agents handle sensitive information safely across multi-tenant architectures.

• Experience: 4+ years of professional backend software engineering experience.
• Backend Framework: Strong proficiency in NestJS for building modular, scalable, enterprise-grade server-side architectures.
• Database Management: Deep understanding of relational data modeling using PostgreSQL, database optimization, and secure query design.
• AI and Search Architecture: Practical, working experience with Vector Databases alongside a strong grasp of embeddings and RAG workflows.
• API Architecture: Masterful understanding of RESTful API design, complex webhooks, asynchronous data loops, and managing API rate limits.
• AI Protocols: Foundational understanding or strong curiosity regarding Model Context Protocol, token budget constraints, and managing conversational state memory.

• AWS Cloud Competency: Experience deploying, monitoring, and working within the AWS ecosystem to manage application layer infrastructure.
• Containerization: Experience using Docker to containerize backend services.