Client Job Description: About the role client is hiring Full Stack
Engineers to build an internal AI platform that streamlines day-to day
work across Sales, Content Marketing, and Engineering. The platform is
the foundation for building, deploying, and governing AI-powered
workflows across the organization. The work includes hosting and
running MCP servers, developing AI workflow capabilities, implementing
secure auth patterns, and enabling teams to safely connect AI agents
to approved tools, systems, and resources. This role suits engineers
excited about practical AI applications, developer platforms, and reusable foundations that help teams work more efficiently.
What You’ll Do
• Build and extend an internal AI platform for creating, deploying,
and managing AI-powered workflows using FastMCP.
•Develop full stack applications and services in Python.
• Build infrastructure to host, deploy, and operate MCP servers.
• Implement secure auth patterns to govern access to tools, data, and
business resources through MCP.
• Design, implement and maintain AI workflows that streamline
repetitive, tedious, or time consuming business tasks using FastMCP
. • Develop secure integrations with internal systems, external APIs,
and AI model providers.
• Build and maintain CI/CD pipelines (GitHub Workflows/Actions) for
reliable, repeatable deployments.
• Use AWS CloudFormation and/or AWS CDK to manage infrastructure as code.
• Collaborate with stakeholders across Sales, Content Marketing, and
Engineering to translate workflows into scalable technical solutions.
• Help define best practices for building, deploying, securing, and
operating AI workflows across the organization.
Python & SQL Must-Haves You will not be a fit without all of the following:
Strong full stack development with Python.
Proficiency with SQL for querying, analyzing, and integrating data
from relational databases.
Proficiency with docker/containerized workloads in AWS.
Building, deploying, and maintaining cloud-based applications on AWS.
E.g. ECS, Lambda, AgentCore.
Infrastructure as code with AWS CloudFormation and/or AWS CDK.
CI/CD pipelines using GitHub Workflows and Actions.
Building AI-powered applications, automations, agents, or workflow tools.
Developing or integrating with MCP servers, tool-calling systems, or
similar plugin architectures.
Hands-on work with AI model APIs and SDKs. Any of the following
FastMCP, OpenAI SDK, Claude SDK, Vercel AI SDK, or AWS Strands.
Active use of AI-assisted coding tools such as Cursor, Claude Code,
Codex, or OpenCode.
Implementing secure authentication, authorization, and access-control patterns.
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