We are seeking a highly skilled Senior Engineer with a strong background in enterprise revenue management platforms and a forward-thinking mindset toward AI-driven technologies. In this role, you will bridge the gap between core enterprise systemsspecifically the Model N platform—and modern AI application development. You will handle production support, system troubleshooting, and backend API integration, while actively leveraging and building AI/ML solutions (such as RAG pipelines and AI agents) to optimize our ecosystem.

Core Responsibilities

- Provide robust technical support and optimization for the Model N platform (Revenue Management, Pricing, and Contracts). Write, debug, and optimize complex SQL and PL/SQL queries.
- Backend & API Integration: Design, build, and maintain secure API integrations using REST/SOAP, OAuth 2.0, and third-party connectors. Work across Java and Python backend systems.
- AI Agent & RAG Development: Build and scale AI agents using frameworks like LangGraph or Model Context Protocol (MCP). Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, focusing on embeddings and search workflows (using tools like Glean or similar enterprise AI search platforms).
- Production Support & Troubleshooting: Act as a tier-3 support anchor in a production environment, diagnosing root causes for system failures, performance bottlenecks, and integration gaps.
- Modern Tooling Adoption: Incorporate AI-augmented development tools (e.g., GitHub Copilot)



into the daily software development lifecycle (SDLC) to accelerate delivery and code quality.

Required Technical Skills & Qualifications

Core Platforms & Backend:

- Overall Experience: 5 to 8 years of qualified software engineering or high-level production support experience.
- Model N Expertise: Minimum 2 to 3 years of hands-on experience with the Model N platform (Revenue Management / Pricing / Contracts modules).
- Database & Languages: Strong proficiency in SQL / PL-SQL, along with development experience in Java and Python.
- APIs & Security: Solid experience with REST / SOAP APIs, OAuth 2.0 protocols, and integrating third-party systems.

AI, ML & Modern Ecosystems:

- AI Engineering: Practical experience building AI agents (LangGraph, MCP) and migrating/integrating Large Language Models (LLMs).
- Data & Search Pipelines: Hands-on experience with Databricks, RAG systems, vector embeddings, and enterprise search platforms like Glean.
- AI Tooling: Demonstrated experience using AI-assisted coding tools (e.g., GitHub Copilot) within the development cycle.
- Concepts: A strong basic understanding of core AI/ML architectures and lifecycle concepts.

Support & Soft Skills:

- Proven track record in a production support environment handling high-priority incidents.
- Exceptional troubleshooting, debugging, and analytical problem-solving skills.
- Strong communication skills to collaborate across technical teams and business stakeholders.

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