AI Practitioner/Forward Deployed Engineer

Build on the Enterprise GPT Platform 

• Design and ship low-code/no-code agents and multi-step workflows for real “jobs to be done.” 

• Use our platform’s Agent Builder, actions, MCP tools, and adjacent automation (e.g., n8n, Power Automate, Zapier/Make). 

Integrate & Orchestrate 

• Connect the Enterprise GPT Platform to systems like Salesforce, ServiceNow, SharePoint/Teams, email, and internal APIs. 

• Orchestrate the platform with external AI tools, services, and models to build a unified, high-impact AI ecosystem for AHEAD. 

• Design and implement MCP (Model Context Protocol) servers and integrations that extend platform capabilities and enable richer, context-aware AI experiences. 

• Implement custom services and integrations (REST APIs, webhooks) when low-code isn’t enough. 

• Ensure solutions are secure, reliable, observable, and compliant in hybrid/SaaS environments. 

Identify & Solve Business Friction Points 

• Proactively surface pain points across AHEAD’s business units and translate them into AI-powered solution opportunities. 

• Partner with stakeholders to prioritize high-impact use cases and build scalable, repeatable solutions — not one-offs. 

• Measure and communicate the business value of solutions delivered (time saved, errors reduced, adoption rates). 

Act as a Forward-Deployed Engineer 

• Embed with business teams, run discovery, and turn fuzzy asks into clear problem statements and MVPs. 

• Manage executive and senior stakeholder engagement: expectations, demos, decisions, and adoption. 

• Rapidly prototype, validate with real users, and harden MVPs into scalable, production solutions. 

Own LLM Quality, Telemetry & Cost 

• Apply production LLM practices: prompt and agent design, guardrails, and evaluation. 

• Instrument usage, reliability, and token/credit consumption at the agent and team level. 

• Use data to improve quality and reduce unnecessary spend (context scoping, summarization, caching, model choice). 

Governance & Lifecycle 

• Identify gaps in platform governance and proactively design guardrails, standards, and controls that reduce business risk and enable safe scaling. 

• Build governance into solutions from the start — not as an afterthought — including access controls, usage policies, auditability, and content safety. 

• Define and maintain promotion standards (naming, ownership, documentation, risk classification) that keep the platform manageable as it grows. 

• Manage solution lifecycle end-to-end: intake → build → test → promote → iterate → retire as needed. 

• Partner with Security, Legal, and Compliance stakeholders to ensure platform practices meet enterprise risk requirements.