Forward Deployed Engineer

Duties/Responsibilities

Build on the Enterprise GPT Platform s

Design and ship agents and multi-step workflows using Glean, Claude, and other GPT platforms and applying platform tools such as Agent Builder, actions, MCP tools, and adjacent automation s (e.g., n8n, Zapier , Make)

Apply AI solution patterns such as retrieval-augmented generation (RAG), workflow orchestration, agent-assisted processes, model integration, API-based automation, and human-in-the-loop review

Integrate & Orchestrate

Create connections to ingest data from enterprise systems like Salesforce, ServiceNow, SharePoint/Teams, email, and internal APIs

Extend platform capabilities through MCP-based integrations and context-aware workflows that improve the usefulness and reach of AI solutions

Implement custom services and integrations, including REST APIs and webhooks, when platform-native patterns or existing automations are not sufficient

Ensure solutions are secure, reliable, observable, and compliant with enterprise standards

Create reusable templates, components, and solution patterns that can be applied across teams and use cases

Identify & Solve Business Friction Points

Proactively surface pain points across business workflows and reimagine them leveraging the best available technology to create impact

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

Partner with stakeholders to prioritize high-impact use cases based on business value, feasibility, risk, and repeatability, with a focus on scalable solutions rather than one-offs

Measure and communicate the value of solutions delivered, including time saved, errors reduced, adoption, reliability, and operational performance

Own LLM Quality, Telemetry & Cost

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

Use test sets, quality metrics, and offline or online evaluation methods to improve solution performance over time

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)

Required Experience

Bachelor’s degree in Computer Science , Engineering, Information Systems, Data and Analytics

Experience in technical roles such as Forward Deployed Engineer, Solutions Engineer, Integration Engineer, Automation Engineer, or similar roles with direct stakeholder engagement

Experience designing or supporting AI-enabled solutions in production environments , prompting and system design, agent development, and workflow configuration

Familiarity with evaluation approaches such as test sets, quality metrics, and iterative improvement loops

Working knowledge of AI solution patterns such as retrieval-augmented generation, orchestration, model integration, vector-backed retrieval, and human-in-the-loop workflows

Ability to c onfigure e xisting connectors t o acce ss required data

Experience integrating enterprise applications using APIs, webhooks, automation tools, or lightweight services

F amiliarity with observability, monitoring, governance , guardrails, and responsible AI controls in enterprise environments

Preferred Experience

Familiarity with common business systems such as Salesforce, Microsoft 365, SharePoint, Teams, ServiceNow, ticketing or ITSM platforms, and CRM or ERP tools

Hands-on experience with Enterprise GPT platforms (e.g., Glean, Claude) and automation tools (e.g., n8n, Zapier, Make)

Familiarity with enterprise security concepts such as RBAC, least privilege, SSO, SAML, OAuth, OIDC, and auditability

Experience working in cross-functional delivery teams that pair solution building with business process transformation, user enablement, and change support

How You Work

Operate as a builder and long-term 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

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

Work effectively as part of a small, embedded team where technical build, business problem framing, and adoption support are closely coordinated