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. 

• Operate as a builder and advisor, not just an implementer. 

• Comfortable being embedded with business teams while upholding platform and security standards. 

• Communicate clearly with executives and non-technical stakeholders about trade-offs, risks, and impact. 

• Move fast on prototypes, but know when to slow down for risk, security, or cost. 

• Stay current on the cutting edge of AI — new models, MCP developments, agentic frameworks, and emerging tooling — and bring relevant innovations back to AHEAD before the market does. 

• Continuously scan the business for friction points and unmet needs; show up with ideas, not just execution. 

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