Rimini Street, Inc. (Nasdaq: RMNI), a Russell 2000® Company, is a proven, trusted global provider of end-to-end, mission-critical enterprise software support, managed services and innovative Agentic AI ERP solutions, and is the leading third-party support provider for Oracle, SAP and VMware software.
Our comprehensive portfolio of unified solutions help run, manage, support, customize, configure, connect, protect, monitor, and optimize enterprise application, database and technology software, enabling our clients to achieve better business outcomes, significantly reduce costs and reallocate resources towards strategic projects.
The Company has signed thousands of contracts with Fortune Global 100, Fortune 500, midmarket, public sector and government organizations who selected Rimini Street as their trusted, proven mission-critical enterprise software solutions provider and achieved better operational outcomes, realized billions of US dollars in savings and funded AI and other innovation investments.
Position Summary
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.
Essential Duties and 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.
Experience
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.