AI Enablement Engineer

Primary Responsibilities

• Embed with internal teams to map workflows, find the largest bottlenecks, and redesign the whole workflow around AI rather than speeding up a single step.
• Build internal tools that replace manual processes.
• Automate multi-step workflows end to end: validation, approvals, notifications, status tracking, and exception handling.
• Prototype in days with AI coding tools and LLMs, then harden what sticks with testing, logging, error handling, and documentation.
• Measure results: cycle time, tickets deflected, hours saved, and error rates.
• Work within security and compliance requirements, including PCI scope, data classification, access control, and change management.
• Train users, gather feedback, and iterate.
• Advise and mentor individual team members on shaping their own workflows, so they can solve more problems themselves.
• Own the long-term support strategy for every tool you ship: who supports it, when it hands off, and when it retires.

AI Enablement & Automation

AI is core to how this role operates. You will:

• Use AI-assisted development tools such as Claude (including Claude Code) daily to design, build, test, and document solutions.
• Build agents and AI-powered workflows on the Microsoft AI stack (Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, Microsoft Fabric), grounded in company data through Microsoft IQ.
• Design, build, and support fully autonomous agents that carry out multi-step work, with guardrails, monitoring, and clear escalation paths.
• Build and maintain MCP servers, both local (STDIO) and remote (HTTP), that securely connect AI tools to internal systems and data.
• Package reusable capabilities as skills and plugins that teams can install and share.
• Practice context engineering: prompt and tool design, retrieval (RAG), and memory, so agents get the right information at the right time.
• Build evals and test sets that measure agent quality, catch regressions, and show when something is ready to ship.
• Instrument agents for observability and cost: tracing, token usage, and failure analysis.
• Choose the right tool and model for each problem, weighing quality, cost, and latency: low-code agent, custom code, or plain automation.
• Scout new AI tools and capabilities as they land, and form a clear point of view on what's worth enabling.
• Apply responsible AI practices in a highly regulated payment-processing environment, including data classification, access control, and human-in-the-loop review.
• Share what works: publish reusable prompts, agents, and patterns other teams can adopt.

Required Background and Experience

• 3-5 years of experience in technical operations/automation, software engineering, or solutions engineering. A Bachelor's degree in a related field or equivalent practical experience.
• Strong business and process sense: you can understand a non-technical team's workflow and design the right fix.
• Keeps pace with an AI ecosystem that changes daily, and can tell a real step forward from hype.
• Clear communicator who can work independently, prioritize by impact, and ship iteratively.
• Ability to manage multiple stakeholders and priorities in a fast-paced environment.

Required Technical Skills

• Hands-on experience building with AI coding assistants, with Claude strongly preferred, and applying LLMs to real workflows.
• Working proficiency with the Microsoft AI stack (for example Microsoft 365 Copilot, Copilot Studio, Foundry, Fabric), or demonstrated ability to ramp quickly.
• Full-stack fundamentals: a modern web framework, REST APIs, SQL, and scripting in Python or JavaScript/TypeScript.
• Comfortable with Git and team workflows: branching, pull requests, and code review.
• Experience with integration and workflow tools (APIs, webhooks, iPaaS, RPA, or similar).
• Understanding of secure development practices, authentication, and access control.

Preferred Skills and Experience

• Payments, fintech, or other regulated-industry experience.
• Familiarity with PCI DSS, SSO/OAuth, and data classification.
• Experience in a Windows/.NET environment (C#, PowerShell), Azure, and CI/CD (Azure DevOps or similar).
• Experience designing and supporting autonomous agents or agentic workflows, including MCP servers, skills/plugins, evals, and multi-agent orchestration.
• Experience supporting production tools used by non-technical teams.