What are the responsibilities and job description for the AI Administrator position at Cyma Systems Inc?
Role : AI Administrator
Location : Houston, Texas
Long Term
Summary :
Embeds directly with the functional to rapidly build and integrate Joule capabilities. Owns the technical build for POCs and production deployments, connecting Joule to surrounding systems and iterating quickly based on direct business feedback.
Key Responsibilities
Design and implement integrations between Joule, SAP S/4 , Datasphere, Integration Suite, and third-party systems such as Microsoft Copilot
Troubleshoot integration and performance issues across the Joule technical stack
Troubleshoot performance issues with AI modules and apply capacity management best practices to optimize Joule and AI Core workloads
Identify AI solutions which can leverage SAP AI Core, SAP BTP, Joule, AI agents, RAG, Knowledge Graphs, vector databases, MCP, tool calling, evaluation frameworks, and AI observability All teams
Define reusable engineering patterns, reference architectures, golden paths, CI/CD standards, and deployment governance for enterprise AI delivery
Define requirements for MCP Servers, and supporting platform services
Leverage SAP AI Hub and SAP AI Gateway to evaluate and integrate foundation models and external AI services
Document technical architecture and hand off supportable solutions to IT
Keep IT informed of technical decisions, dependencies, and risks
Leverage SAP AI Hub and SAP AI Gateway to evaluate and integrate foundation models and external AI services.
Determine integration requirements between SAP applications, MCP services, business data sources, and external AI providers.
Experience integrating Joule with other chat bots like Copilot, etc
Work closely with SAP developers, BTP administrators, integration specialists, AI engineers, architects, and functional teams to deliver solutions.
Qualifications
Experience with SAP Joule, BTP, or similar AI/copilot platforms
Familiarity with SAP Datasphere and enterprise data integration patterns
Deep understanding of LLM application architecture, including agentic AI, RAG/GraphRAG, Knowledge Graphs, vector databases, MCP, tool calling, evaluation frameworks, and AI observability
Strong software engineering foundation, including Python, modern enterprise application architecture, Kubernetes, Docker, CI/CD, DevSecOps, hyperscaler platforms, and distributed systems
Experience with SAP BTP and MCP Server concepts and architectures
Comfortable working in ambiguous, fast-moving environments typical of forward-deployed roles
Strong communication skills to work directly with non-technical business stakeholders
Experience with Microsoft Copilot or similar enterprise AI integrations is a plus
Experience with SAP document grounding and how it can be leveraged for various AI scenarios, and familiarity with SAP Business Data Cloud (BDC) AI use case scenarios
Broad, adaptable skillset spanning software engineering, data engineering, and customer engagement, with comfort working across varied business domains and engaging both technical teams and executive stakeholders