AI Practitioner/Forward Deployed Engineer

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OtherForward Deployed Engineer

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Requirements Summary

Prompt/system design and agent development Evaluation frameworks (test sets, metrics, offline/online evals) Deployment and operation at scale

Technical Tools

OtherForward Deployed Engineer

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.

At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.

We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.

We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.

The AI Practitioner (Enterprise GPT Platform) is a hands-on, customer-facing role on the Enterprise Insights & GPT Platform team. You will design, build, and run production AI applications on our Enterprise GPT Platform — custom agents, workflows, connectors, and integrations — that fundamentally transform the way AHEAD does work. You’ll orchestrate our platform with best-in-class AI tools and emerging technologies to create a cohesive, scalable AI ecosystem. You own the full solution lifecycle: discovery, design, build, rollout, and ongoing optimization.

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.

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 (Enterprise GPT Platform experience strongly preferred; others like Power Automate, n8n, Zapier/Make, ServiceNow, Salesforce Flows are helpful).

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.