Sr Developer - Python/LLMs/RAG/AI Agents

Job Summary

If you need stable, well-defined requirements before you can start, this role will be uncomfortable. If youd rather supervise an AI than trust it blindly, and you want to be the technical bridge between a complex accounting domain and an AI platform, keep reading. Youll take end-to-end technical ownership of critical systems and contribute to engineering excellence across the team. Youll be the technical bridge between GHRAs domain logic (royalty, production volumes, ownership allocation) and the AI platform layer - translating accounting business rules into context that agents can reason over reliably. Mentoring less-experienced engineers, guiding design and code quality, and building strong technical capabilities within the team are all part of the role. The team operates across global, cross-functional groups in a culture that values continuous improvement, operational excellence, and knowledge sharing.

What youll bring

• You have deep experience in distributed systems and scalable application design, with strong hands-on skills in LLMs, advanced RAG patterns, and agentic workflows.
• Experience building agentic workflows on SAP AI Core and integrating LLM-based features into CAP Java applications is expected.
• Proficiency in system design for scale, resiliency, and fault tolerance is required.
• Strong experience in performance optimization, cost management, and troubleshooting complex production systems is essential.
• A solid understanding of SRE principles, observability (logs, metrics, tracing), and incident management is expected - including Open Telemetry instrumentation and SAP Cloud Logging dashboards.
• Experience designing evaluation frameworks for AI systems, validating model behavior, and applying agentic engineering patterns is critical.
• You use Claude-style tools to generate code and ideas, but you read and challenge their output - you know when the AI is making the wrong call in both code and design.
• Familiarity with multi-tenant SaaS architecture on SAP BTP (MTX, service-manager, IAS/AMS) is a strong advantage.
• Four values matter at every level: Learning agility (non-negotiable, and the most important value) - you rapidly pick up new models, frameworks, SAP Technologies, ontologies, and industry processes. You enjoy updating your mental model as the landscape shifts, which it will, frequently.
• Learning agility (non-negotiable, and the most important value) - you rapidly pick up new models, frameworks, SAP Technologies, ontologies, and industry processes. You enjoy updating your mental model as the landscape shifts, which it will, frequently.
• Bias for action - you move quickly from ambiguity to a working prototype and learn through execution.
• Critical thinking - you ask the right questions, so we stay focused on the objective instead of just managing the day.
• End-to-end ownership - you own the outcome from problem definition through build, deployment, and evaluation.

What youll build

• Youll independently design and architect scalable, AI-native application systems and components, including advanced RAG pipelines and multi-agent workflows.
• Youll design and build AI agents integrated into GHRAs revenue accounting domain - including formula explanation, disposition computation assistance, and production volume analysis - using SAP AI Core and Python as primary programming language, CAP in Java will be secondary programming language.
• Youll contribute to the design of ontology-grounded agents for the domain, encoding accounting rules, formula structures, and ownership relationships as structured knowledge that agents can reason over reliably, rather than relying on prompt-only approaches.
• Evaluation and benchmarking frameworks to measure AI system quality, reliability, and performance are things youll build and own. This includes domain-specific test harnesses that validate LLM outputs against known good formula results and accounting rules - and the honest memo that explains why an idea shouldnt ship.
• Youll optimize latency, throughput, and cost efficiency across systems - including HANA Cloud query optimization and AI Core inference cost management.
• Observability and SRE practices, including monitoring, alerting, incident response, and root cause analysis, are areas youll implement and improve - specifically enabling OTEL tracing across the CAP microservice fleet and building Cloud Logging dashboards for cross-service visibility.
• Youll partner with platform, data, and AI teams to design robust, production-ready solutions and improve system integration. System scalability, resilience, and maintainability are areas youll continuously work to strengthen.

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