AI and Automation Developer

Role Summary

The builds AI and automation solutions that operate in production, supporting internal operations and client engagements.

The role is responsible for technical implementation within established architecture and engineering standards. Responsibilities include building integrations, AI agents, workflows, lightweight applications, and orchestration solutions; testing and instrumenting implementations; participating in peer reviews; deploying solutions; and supporting them after go-live.

All positions carry the same title, level, and responsibilities. There is no junior or senior tier within the group. The role reports to the AI and Automation Lead.

Required Competencies

• Strong production engineering judgment rather than experience limited to prototypes or demonstrations.
• Ability to read, modify, debug, and reason about code in at least one mainstream programming language.
• Ability to understand unfamiliar APIs and correctly implement authentication, permissions, scoping, and failure handling with minimal guidance.
• Comfortable working with both code-first and low-code automation approaches, selecting the appropriate solution based on the business and technical requirements.
• Willingness to take ownership of solutions after deployment, including troubleshooting, support, and documentation.
• Ability to conduct meaningful peer reviews and incorporate feedback from other developers.
• Strong written English communication skills, with the ability to explain technical concepts clearly to colleagues and, when needed, clients.

Environment and Tools

The core competency is connecting systems so that AI agents and automated workflows can interact with them securely and effectively.

The role may work through APIs, configure MCP servers, build integrations, or use automation platforms to interact with business systems. Because client environments may introduce unfamiliar technologies, the ability to quickly understand and work with new systems is more important than deep expertise in any single platform.

Required and Transferable

• Proficiency in at least one mainstream programming language: Python, JavaScript, TypeScript, or C#.
• Experience with REST APIs, webhooks, authentication, and authorization, including delegated and application permissions.
• Experience building or configuring MCP servers, or equivalent experience safely exposing system capabilities to AI agents.
• Familiarity with source control, branching, peer review, CI/CD, and secrets management.
• Practical experience with LLMs and AI agents, including prompting, grounding, tool calling, orchestration, and evaluation.

Systems in the Environment

Experience with the following is beneficial but not mandatory, as training can be provided:

• ConnectWise Manage and BMS
• Microsoft 365 and Microsoft Graph
• Azure, including Functions and Logic Apps
• Microsoft Fabric
• Microsoft Power Platform and Copilot Studio
• RMM tools and Power BI
• RPA and automation platforms such as Rewst
• Azure DevOps and comparable engineering platforms

The role will work across modern LLM and agent platforms. Candidates should be adaptable and comfortable learning new technologies rather than being dependent on a single vendor or platform.

Key Responsibilities

• Build and maintain connectors and integrations against REST APIs, Microsoft Graph APIs, and webhooks to make business systems available for AI agents and automated workflows.
• Build or configure MCP servers where AI agents require secure and appropriately scoped access to systems and data.
• Develop cross-application automation using Microsoft Power Platform, Azure Logic Apps, Azure Functions, Microsoft Fabric, and RPA platforms such as Rewst.
• Design and implement agentic workflows using tool calling, prompt chaining, orchestration, retry and fallback logic, grounding, and appropriate guardrails.
• Develop repeatable evaluation methods so AI agent behavior can be tested and measured rather than simply demonstrated.
• Instrument production workflows to provide visibility into operational health, performance, and intended outcomes.
• Write and execute automated and manual tests.
• Participate in peer reviews and incorporate feedback into implementations.
• Provide release evidence and implementation updates to the AI and Automation Lead.
• Deploy and operate solutions, including logging, telemetry, troubleshooting, and defect remediation.
• Create implementation guides, configuration documentation, and support handoffs to ensure solutions can be maintained by other team members.

Qualifications

• Demonstrated software development experience in at least one mainstream language such as Python, JavaScript, TypeScript, or C#.
• Working knowledge of APIs, REST integrations, webhooks, authentication and authorization, and error-handling practices.
• Practical experience building automation, integrations, or applications that have been used in production rather than experience limited to notebooks, prototypes, or demonstrations.
• Practical experience with large language models, including prompting, grounding, tool calling, agent or workflow orchestration, and evaluation.
• Comfortable with source control, peer review, testing, and task management.
• Basic understanding of cloud environments and production operations, including identities, secrets, logging, deployment, and monitoring.

Nice to Have

• Microsoft 365 and Microsoft Graph integration experience
• Azure Functions or Azure Logic Apps
• Microsoft Fabric
• Microsoft Copilot Studio
• Code-first AI/agent frameworks
• CI/CD experience
• Observability and monitoring
• Managed service provider or professional services experience

What Success Looks Like in the First 90 Days

• Successfully ramps up on the technology and delivery environment and begins contributing to live projects within the first four weeks.
• Delivers at least one meaningful automation or AI workflow through development, testing, peer review, release approval, and production handoff.
• Produces solutions with sufficient telemetry and documentation for another team member to support and maintain them independently.
• Uses AI-assisted coding tools effectively while still being able to independently explain, debug, test, and validate the resulting code.
• Demonstrates the ability to learn unfamiliar systems and APIs quickly and apply that knowledge to production implementations.