Forward Deployed Engineer

Duties/Responsibilities

Build on the Enterprise GPT Platforms 

• 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 automations (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 configure existing connectors to access required data 

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

• Familiarity 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