AHEAD
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The Forward Deployed Engineer (FDE) is a hands-on role embedded within AHEAD's internal AI transformation function. You will design, build, and run production AI applications on enterprise GPT platforms, including custom agents, workflows, connectors, and integrations. You will operate as the technical build resource within a cross-functional delivery team, partnering closely with business stakeholders, a transformation consultant, and an adoption lead to support one of AHEAD’s key domains, such as Services, Go-to-Market, or Corporate & Technology. This person owns the full solution lifecycle from discovery and design through build, rollout, optimization, and ongoing support.
This role is well suited for someone who enjoys working directly with business teams, translating ambiguous needs into practical AI-enabled solutions, and building production AI patterns such as orchestration, model integration, evaluation, observability, and secure enterprise deployment. Success in the role requires comfort working inside a shared team model where solution design, user adoption, and business outcomes are delivered in close coordination rather than in isolation.
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)
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
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
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
After you apply, unlock the direct contact details of the people who actually make the call. A quick follow-up makes you 5x more likely to land an interview.
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