Agentic AI Engineer

About Rimini Street, Inc.

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.

We are actively seeking a Agentic AI Engineer . This role is based in India.

About Rimini Street, India, GCC.

Rimini Street Inc, HQ : Las Vegas, NV, USA a disruptor in third party ERP support services, established undisputed leadership and as a natural progression, entered India with Rimini Street, India GCC India kick starting operations in Hyderabad , in 2013 with Global Client Onboarding Services, IT shared services and Global Service Development. In no time, Rimini Street, India GCC started Bengaluru operations going up the value chain with more complex product development (Oracle, SAP, Peoplesoft, JDE etc.) & advanced services (Managed services, Professional services, Security Managed Services etc).

Rimini Street, India GCC gained valuable share in bringing the reputation to Rimini Street Inc of being a global provider of unified support and managed service solutions for enterprise software. Today, Rimini Street, India GCC is a family of about 800+ full time talented individuals, thanks to the remarkable talent that has supported the expansion.

Rimini Street, India has nicely emerged as Global Capability Centre (GCC), and proudly says, “if you are best of the best, you belong to Rimini”. We are on a mission to contribute significantly to our “Rimini ONE” program, a turnkey Rimini Street service program that offers a comprehensive set of unified, integrated services that can run, manage, support, customize, configure, connect, protect, monitor, and optimize your Oracle and SAP ERP, database, and technology software.

Role Overview

The Agentic AI Engineer is responsible for designing and building the cognitive architecture of AI agents that interact with enterprise ERP systems. This is not prompt engineering — it is the design of agent behavior systems: how agents reason through ambiguous multi-step problems, when and how they use tools, how they coordinate with other agents, how they govern themselves within risk-tiered autonomy frameworks, and how they escalate to humans at the right moments.

This role sits at the intersection of AI engineering and enterprise systems architecture. Where the GenAI Engineer (junior/mid-level) focuses on individual prompt design, context window management, and LLM API integration, the Agentic AI Engineer designs the higher-order systems: multi-agent coordination patterns, cognitive reasoning chains for complex ERP workflows, dynamic tool selection strategies, autonomous vs. supervised decision boundaries, and the feedback loops that make agents learn from their operational experience.

This is an emerging discipline. The industry is evolving from simple prompt engineering toward what might be called agent behavior engineering, agentic systems design, or cognitive architecture. We are looking for someone who can help define what this role becomes.

Key Responsibilities

Agent Cognitive Architecture

• •Design the reasoning framework for how agents decompose complex ERP tasks into structured decision trees with governance checkpoints at each stage.
• •Build agent personas and behavioral profiles for each Rimini Solution domain (Finance, Procurement, Supplier Management, Expense, Support) defining domain-specific reasoning patterns, risk tolerances, and escalation triggers.
• •Architect dynamic tool selection strategies where agents choose which MCP tools to invoke based on context, confidence, and task requirements rather than hardcoded sequences.
• •Design conversation state management for long-running agent sessions that span multiple interactions, tool calls, and human-in-the-loop approvals.
• •Build confidence scoring frameworks that translate LLM output uncertainty into actionable governance decisions (proceed, verify, escalate, halt).

Multi-Agent Coordination

• •Design A2A (Agent-to-Agent) communication patterns for collaborative workflows where multiple specialized agents contribute to a single business outcome.
• •Architect supervisor/worker agent hierarchies with clear delegation, progress monitoring, and result aggregation patterns.
• •Build conflict resolution strategies for when agents produce contradictory assessments (e.g., risk agent says halt, efficiency agent says proceed).
• •Design shared context and memory patterns that allow agent teams to build collective understanding of a business situation without redundant processing.

Autonomous Decision Governance

• •Translate OPA policy definitions into agent-actionable decision boundaries — bridging the gap between policy-as-code and agent reasoning.
• •Design the autonomy progression model: how agents earn increased autonomy through demonstrated accuracy, how trust degrades after errors, and how the system self-corrects.
• •Build the “explainability layer” that enables agents to articulate their reasoning chain to human reviewers in business-meaningful terms.
• •Design circuit breaker patterns at the cognitive level agents that recognize when they are outside their competence boundary and proactively escalate rather than guessing.

Knowledge Integration & Institutional Memory

• •Design retrieval-augmented reasoning patterns where agents dynamically pull relevant knowledge from vector stores, historical and knowledge data and approval pattern databases during their reasoning process.
• •Build the feedback mechanisms that capture how experienced users correct or override agent recommendations, turning those corrections into improved future reasoning.

Agent Evaluation & Quality

• •Design evaluation frameworks for agent behavior — not just output accuracy but reasoning quality, appropriate escalation, governance compliance, and user trust metrics.
• •Build scenario-based testing methodologies: synthetic ERP scenarios that test agent behavior across edge cases, ambiguous situations, and adversarial inputs.
• •Define agent performance metrics: task completion rate, escalation accuracy, false positive/negative rates for autonomous decisions, time-to-resolution, and user override frequency.
• •Own the continuous improvement loop: analyze production agent behavior, identify reasoning failures, and refine cognitive architecture to prevent recurrence.

Required Experience

• •4-6 years of software engineering experience with strong Python and/or Java proficiency.
• •3+ years working with AI/ML systems in production, including LLM-based applications.
• •1+ years designing or building agentic AI systems — autonomous agents that reason, plan, use tools, and take actions (not just chatbots or simple RAG).
• •Demonstrated experience with agent frameworks: LangChain/LangGraph, Pydantic AI, CrewAI, AutoGen, Semantic Kernel, or equivalent.
• •Experience with tool-calling / function-calling patterns in LLMs (MCP, OpenAI function calling, Anthropic tool use).
• •Understanding of evaluation methodologies for non-deterministic systems — testing outputs that aren't binary right/wrong.
• •Experience building systems that handle human-in-the-loop workflo