Forward Deployed Engineer

Nelnet•Lincoln, NE

37d•$140,000 - $185,000•Remote

About The Position

This role is the primary technical bridge between Nelnet’s GenAI delivery team and the higher education and SLED (state, local, and education) institutions it serves. The Forward Deployed Engineer works directly with client technical stakeholders to scope solutions built on the Gemini Enterprise Agent Platform (Google’s 2026 evolution of Vertex AI), translating requirements and technical compliance considerations into a scoped delivery backlog, and ensures the solutions being built map to real institutional needs rather than generic platform capabilities. This role works in partnership with an Engagement Manager, who owns the overall client relationship, contract and commercial terms, staffing, and engagement reporting to practice leadership. The Forward Deployed Engineer owns the technical relationship: solution scoping, delivery backlog, and technical risk. This division of labor differentiates Nelnet’s GenAI practice from a standard Google Cloud reseller relationship, pairing commercial accountability with deep Google Cloud technical and domain credibility rather than folding both into a single role.

Requirements

Hands-on experience scoping and delivering GenAI/agentic AI solutions for enterprise or institutional clients — required. Direct experience with the Gemini Enterprise Agent Platform (Agent Builder, Model Garden, Workspace Studio, Gemini models) strongly preferred; candidates with deep GenAI delivery experience on other platforms (e.g., AWS Bedrock, Azure AI Foundry) who can ramp quickly on Google's stack will be considered.
Working familiarity with cloud data, compute, and compliance concepts used in AI solution design (e.g., data warehousing, container/serverless compute, IAM, network security controls) sufficient to speak credibly to Google Cloud's specific implementations.
Experience in a client-facing technical role (solutions engineering, forward deployed engineering, technical consulting, or similar) delivering AI/ML solutions directly with enterprise or institutional clients.
Demonstrated ability to translate ambiguous client requirements into a structured, sequenced delivery backlog.
Familiarity with compliance and regulatory considerations relevant to education or public sector data (e.g., FERPA, state privacy law, procurement requirements).
Strong written and verbal communication skills, including experience leading client discovery and requirements sessions.
Ability to work cross-functionally with engineering teams to ensure delivery commitments are realistic and technically grounded.
Comfortable operating in a paired delivery model with an Engagement Manager, owning technical scope and direction while deferring on commercial and account-relationship decisions.

Nice To Haves

Direct experience with the Gemini Enterprise Agent Platform (Agent Builder, Model Garden, Workspace Studio, Gemini models) strongly preferred
Google Cloud certification preferred (Professional Cloud Architect or equivalent technical certification), demonstrating hands-on platform fluency; foundational/business-oriented certifications (e.g., Generative AI Leader) do not satisfy this preference.
Higher education or SLED (state, local government, and K-12/education) domain experience strongly preferred.
Demonstrates sound judgment in ambiguous client situations involving incomplete information and competing priorities.
Builds credibility quickly with both technical and non-technical stakeholders.
Anticipates technical, compliance, and delivery risks before they surface, taking proactive action.
Balances client advocacy with delivery feasibility and team capacity.
Communicates complex technical concepts in terms non-technical stakeholders can act on.
Navigates client-facing situations, including difficult conversations, with professionalism and confidence.
Exercises influence across internal delivery teams without relying on positional authority.
Demonstrates ownership of technical engagement outcomes from scoping through delivery.
Works effectively in a paired model with an Engagement Manager, respecting the boundary between technical and commercial ownership.
Seeks continuous improvement through client feedback and delivery retrospectives.

Responsibilities

Lead technical discovery with client stakeholders, translating institutional pain points into scoped, deliverable use cases on the Gemini Enterprise Agent Platform.
Run requirements-gathering sessions to define technical scope, success criteria, and delivery timelines, in coordination with the Engagement Manager.
Assess technical feasibility during the pre-sales cycle to help right-size proposed engagements; the Engagement Manager owns the commercial shape of the deal.
Build and maintain the delivery backlog, converting client requirements into clear, sequenced work for the GCP/Gemini Enterprise Engineer, Data Engineer, and AgentOps Engineer.
Prioritize backlog items against client timelines, contract commitments, and technical dependencies.
Serve as the connective layer between client-facing technical commitments and internal delivery capacity.
Validate that delivered solutions meet the acceptance criteria defined during scoping, serving as the client-facing quality gate before an engagement is considered complete.
Identify and surface technical and data compliance requirements specific to higher education and SLED environments (e.g., FERPA data handling, state data privacy statutes, accessibility requirements) early in the solution design process.
Flag data governance, security, and technical risk to the Engagement Manager before it affects delivery timelines or client trust; contract-level and commercial compliance remain with the Engagement Manager and legal.
Ensure solution designs account for institutional compliance obligations rather than retrofitting them after build.
Bring credible, first-hand understanding of higher education or SLED operating environments to client conversations.
Advise clients on realistic AI adoption paths given institutional constraints such as data readiness, system integration limits, and technical staff capacity.
Build trust with client technical stakeholders as a peer, not just a vendor representative.
Work closely with the GCP/Gemini Enterprise Engineer, Data Engineer, and AgentOps Engineer to ensure delivery plans are technically sound and achievable.
Partner with the Engagement Manager, providing technical status, risks, and backlog visibility so the Engagement Manager can manage the overall client relationship and report to practice leadership.
Partner with the SLED Account Executive and GTM lead to inform repeatable offerings based on field learnings.