AI Practitioner/Forward Deployed Engineer

AHEAD

20h•$95,000 - $115,000

About The Position

The AI Practitioner (Enterprise GPT Platform) is a hands-on, customer-facing role on the Enterprise Insights & GPT Platform team. This position involves designing, building, and running production AI applications on AHEAD's Enterprise GPT Platform, including custom agents, workflows, connectors, and integrations, to fundamentally transform the way AHEAD operates. The role requires orchestrating the platform with best-in-class AI tools and emerging technologies to create a cohesive, scalable AI ecosystem. The AI Practitioner owns the full solution lifecycle, encompassing discovery, design, build, rollout, and ongoing optimization.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, Data/Analytics, or equivalent experience.
Experience in technical roles such as AI Practitioner, Forward Deployed Engineer, Solutions Engineer, Integration Engineer, Automation Engineer or similar, with direct stakeholder engagement.
Production LLM experience, including: Prompt/system design and agent development, Evaluation frameworks (test sets, metrics, offline/online evals), Deployment and operation at scale (multiple teams/use cases).
Full-Stack Programming Skills: Ability to handle both client-side (frontend) and server-side (backend) development, ensuring a complete, functional application.
Custom Integrations: Experience creating unique solutions to connect different software applications (e.g., using APIs to make an internal database talk to a CRM tool).
Track Record of Shipping Production Software: A documented history of building and releasing software that is actually used by real customers, not just in testing.
Familiarity with enterprise systems: Security frameworks (RBAC, least privilege, SSO/SAML/OAuth/OIDC, auditability), Common business stacks (e.g., Salesforce, M365/SharePoint/Teams, ServiceNow, ticketing/ITSM, CRM/ERP).
Hands-on experience with low-code/no-code or automation platforms.

Nice To Haves

Enterprise GPT Platform experience (strongly preferred).
Experience with other low-code/no-code or automation platforms like Power Automate, n8n, Zapier/Make, ServiceNow, Salesforce Flows (helpful).

Responsibilities

Design and ship low-code/no-code agents and multi-step workflows for real "jobs to be done."
Use the platform's Agent Builder, actions, MCP tools, and adjacent automation (e.g., n8n, Power Automate, Zapier/Make).
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.
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).
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.
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).
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.