AI Systems Developer (Fastapi + MCP + LLM Orchestration)

About Us

We’re a B-Corp certified full-service marketing agency based in Vancouver, Canada. Our team builds AI-powered SEO and content automation systems that power research, keyword discovery, strategy generation, and automated website optimization for clients across North America. Our infrastructure combines modern backend systems, LLM orchestration, and deep integrations with Webflow, DataForSEO, and cloud services to deliver scalable, intelligent automation.

We’re currently looking for a highly skilled and reliable AI Systems Developer who can own and evolve our backend services, MCP server, and LLM-driven automation pipelines. You’ll work closely with our engineering and strategy teams to build scalable systems that integrate AI models with external tools and APIs.

Role Overview

As an AI Systems Developer, you’ll be responsible for owning and maintaining our AI-powered SEO and content automation infrastructure end-to-end. This includes maintaining and evolving our FastAPI backend (arb-v1) and Python MCP server (keyword-mcp), both deployed on Railway, which together power keyword discovery, client research, strategy generation, and automated Webflow SEO optimization.

You’ll work across backend services, MCP tool design, and LLM orchestration workflows, ensuring systems are reliable, performant, and production-ready. This role involves implementing new AI-driven workflows, improving existing pipelines, integrating third-party APIs, and supporting streaming chat and automation capabilities within our platform.

This is a full-time remote position ideal for a developer who thrives in a fast-moving, AI-first environment and enjoys owning systems end-to-end.

Key Responsibilities

Maintain and evolve the FastAPI backend and Python MCP server powering AI automation workflows.

Design and implement MCP tools that allow LLMs to safely and reliably call external APIs (DataForSEO, Webflow, and others).

Build and maintain LLM-driven workflows using the OpenAI Responses API, embeddings, vector stores, and tool-calling.

Develop and maintain RAG-based pipelines using vector stores, file search, and contextual retrieval.

Implement streaming chat pipelines and agent workflows for frontend integration.

Manage PostgreSQL databases using SQLAlchemy and Alembic.

Deploy, configure, and maintain services on Railway, including environment variables and service-to-service communication.

Integrate external services such as Webflow, DataForSEO, and AWS S3.

Refactor and optimize backend services for clarity, performance, and reliability.

Troubleshoot production issues, improve resilience (timeouts, retries, backoff), and ensure system stability.

Collaborate with internal teams to design and implement new AI-powered features and automation workflows.

To Apply, please send

A short introduction describing your experience with AI systems, LLM integrations, and backend development.

Links to relevant projects, GitHub repositories, or production systems you’ve built.

Your availability and working hours overlap with PST.

Required Skills & Experience

Familiar with Website creation platforms like Webflow

Strong experience with Python backend development (FastAPI or similar frameworks).

Proven experience working with LLM APIs, especially OpenAI Responses API, tool-calling, and embeddings.

Experience building and maintaining MCP servers or similar tool-calling / agent integration systems.

Experience working with PostgreSQL and ORMs such as SQLAlchemy.

Experience deploying and maintaining production services in cloud environments (Railway, AWS, or similar).

Strong understanding of API design, async systems, and backend architecture.

Experience building multi-step agent workflows and automation pipelines.

Ability to own systems end-to-end with minimal supervision.

Strong debugging skills and attention to reliability and performance.

Excellent communication skills and accountability for deliverables.

Bonus Skills

Experience with FastMCP or the Model Context Protocol (MCP).

Experience building RAG systems using vector stores and embeddings.

Experience integrating Webflow, DataForSEO, or similar APIs.

Experience with streaming APIs and real-time frontend integrations.

Experience with AWS services such as S3.

Experience working with Railway or similar deployment platforms.

Familiarity with prompt engineering, tool design, and safe LLM orchestration.