Forward Deployed Engineer (Agentic AI)

Rimini Street

9d•$140,000 - $180,000•Remote

About The Position

Reporting to the VP, Innovation, Solution Delivery, the Forward Deployed Engineer (Agentic AI) embeds with enterprise clients to design, build and operationalize agentic AI solutions in production. You will build production outcomes for clients, integrate agentic systems with enterprise platforms and systems of record, and stay accountable through cutover, early production and handover to ongoing support. The role is platform-agnostic by design and adapts to whichever AI platform best fits the client, including hyperscale AI services, ServiceNow and other enterprise AI platforms.

Requirements

This is a Principal-level role.
We are looking for engineers with the depth to operate independently inside enterprise client environments, the breadth to span production engineering and applied AI, and the demonstrated ability to ship in a discipline that is still being defined.
We weight recent, hands-on agentic AI work heavily, recognizing that production agentic systems are a young discipline.
Equivalent experience from adjacent disciplines, including machine learning engineering, applied AI research with production deployment exposure, or senior backend engineering with recent agentic shipping, is welcomed.
8+ years of professional engineering experience across production systems, with significant production rather than purely advisory time.
2+ years shipping AI or machine learning systems into production, with demonstrated hands-on agentic or LLM-based work.
Proven track record working directly with Fortune 500 or Global 2000 enterprise clients in ambiguous, complex environments.
Track record of operating across the full lifecycle of a system, including architecture ownership, handover to support and ongoing escalation support.
Demonstrated ability to learn fast and ship in emerging technical disciplines.
Candidates whose recent agentic AI work was learned quickly on the back of strong fundamentals are explicitly in scope.
ERP and enterprise systems experience across SAP, Oracle E-Business Suite, JD Edwards, Infor, ServiceNow, Salesforce, Workday or other systems of record.
Direct SAP and Oracle EBS experience highly valued.
Experience with multiple-agent AI platforms, demonstrating ability to deliver on whichever platform the client has standardized on.
Prior experience as a Forward Deployed Engineer, Applied AI Engineer or equivalent embedded delivery role at an AI lab, hyperscale or systems integrator.
Production Python and at least one additional enterprise or full-stack language such as TypeScript, Java, Go, C# or Scala, including modern agent frameworks and patterns.
SQL and data modelling fluency, including the ability to reason about enterprise data quality, lineage, permissions and operational semantics.
Hands-on retrieval-augmented generation, including chunking, embeddings, vector stores and reranking.
Evaluation suite design for LLM-based systems, including regression detection, hallucination measurement and grounding checks.
Production observability for AI systems, including latency, token usage, error rate and output drift instrumentation.
Working knowledge of at least one major agentic AI platform such as AWS Bedrock, Azure AI, Google Vertex AI, ServiceNow AI and willingness to learn others as engagements demand.
Agent tool execution and governance, including the Model Context Protocol (MCP) or equivalent tool interfaces, permission scoping, scoped credentials, approval gates, audit trails and safe execution controls.
Enterprise integration fundamentals, including REST and event-driven patterns, identity flows such as OIDC and SAML, and secure deployment into client-controlled environments.
UI and front-end fluency in modern web frameworks such as Angular, React or equivalent, sufficient to build or extend the user-facing layer of agentic solutions.
Durable execution frameworks, event-driven architectures or workflow orchestration at enterprise scale.
Exceptional written and verbal communication across deep technical and executive audiences, with strong consultative and outcomes-driven instincts.
High autonomy, resilience and pragmatism in legacy and politically complex environments, with the proactive discipline needed for effective remote embedding.
Bachelor’s degree in computer science, engineering, information systems or related field, or equivalent practical experience.

Responsibilities

Lead technical discovery, scope agentic AI use cases, and translate business problems into engineering deliverables with measurable success criteria such as cycle-time reduction, support deflection, automation rate, security and governance success rate and adoption rate.
Design target architectures that fit the client's environment, regulatory posture and platform preferences, selecting the right substrate per engagement across hyperscale AI services, ServiceNow and other enterprise AI platforms.
Write production-grade code to deliver the solution, working inside the client's source control, CI/CD and deployment infrastructure, and within enterprise release governance including change approvals, separation of duties, audit evidence and rollback planning.
Build and tune retrieval pipelines, prompt architecture, guardrails, agent orchestration and human-in-the-loop controls that hold under real production variation.
Implement enterprise-safe agent tool execution, including permission scoping, approval gates, audit trails and rollback paths for any agent action that touches systems of record.
Build evaluation suites that catch hallucinations, regressions, grounding gaps and quality drift, and implement production observability for latency, token usage, error rates, accuracy and output drift.
Integrate solutions with client identity, secrets management, network controls, incident response and compliance tooling.
Stay engaged through cutover and the first production iteration cycle, then run a structured handover to the ongoing support team, including documentation, runbooks and knowledge transfer.
Act as an escalation backup to the support team after handover, stepping back in when issues exceed the support team's depth or when the system needs architectural intervention.
Build trusted advisor relationships with client engineering, data, security and business stakeholders, and communicate clearly to both engineering teams and C-level audiences.
Abstract field learnings into reusable patterns, accelerators and reference architectures that feed back into Innovation and shape product direction, methodology and the next engagement.