Developer

SAP ASIA PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

SAP Asia PTE. LTD. seeks engineers who can move from prototype to production in AI-native engineering. You will own end-to-end AI prototypes, work with product teams, and supervise Claude-style code generators while applying architectural judgment to scalable solutions.

You’ll collaborate in a small pod with the CTO for Industry AI ENR, tackling ontology-driven agents and ahead-of-the-curve AI initiatives that push SAP technologies forward.

Qualifications

• 3 - 6 years of hands-on experience with LLM APIs, at least one agentic orchestration framework, RAG pipelines, prompt engineering, and evals.
• Comfort with Claude-style tools to generate code and ideas, but you read and challenge their output.
• Learning agility to rapidly pick up new models, SAP technologies, ontologies, and industry processes.
• Bias for action and end-to-end ownership from problem definition through build, deployment, and evaluation.

Responsibilities

• Own modules and prototypes end-to-end and collaborate with pod peers.
• Spend real time embedded with product teams on focused AI missions.
• Prototype, validate, and productionize AI-driven solutions with strong architectural judgment.

Skills

LLM APIs

Agentic orchestration

RAG pipelines

Prompt engineering

Eval frameworks

Python

Tools

Claude-style tools

TypeScript

Terraform

BTP

CAP

HANA Cloud

Joule Studio

Fiori

Job description

What you'll build

We move across the prototype-to-production spectrum: we build fast, validate honestly, and partner with platform and product teams to scale what proves valuable. The stack varies — Python, TypeScript, Terraform, BTP, Joule Studio, CAP, Fiori, whatever the mission needs — but we care far more about your taste for good code, your ability to supervise Claude-style code generators and AI brainstormers, and your architectural judgment than about fluency in any single language.

You’ll use tools like Claude as accelerators, but you’ll read what they produce, challenge their choices, and know when the AI is making the wrong call — both in code and in idea generation.

You’ll own modules and prototypes end-to-end, pair with other builders in the pod, and spend real time embedded with product teams on focused AI missions.

What you'll bring

• 3 - 6 years of hands-on experience with LLM APIs, at least one agentic orchestration framework, RAG pipelines, prompt engineering, and evals (human, LLM-as-judge, automated), plus a strong sense for good vs. bad code.
• You use Claude-style tools to generate code and ideas, but you read and challenge their output. Comfort with Python helps; enjoyment in learning new stacks (BTP, CAP, HANA Cloud, Joule, TypeScript, Terraform) matters more.
• Learning agility (non-negotiable, and the most important value in this pod) — 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.

Where you belong

You’ll join Applied AI Builders, a small, hands-on pod that works closely with the CTO for Industry AI ENR. Our job is to take ambiguous problems, half-formed ideas, and evolving initiatives, and turn them into prototypes, proofs, and production-ready outcomes. Some weeks we’re embedded with a product team, partnering on a specific AI mission. Other weeks we’re stress-testing a raw idea to see whether it deserves to be built, adapted, or respectfully retired.

The pod’s missions sit at the frontier of AI-native engineering. One current mission: ontologies. We’re designing and evolving ontologies for our domains, building agents on top of them, and proving — with data — how ontology-grounded agents compare to raw prompt-and-MCP-based agents. We’re defining the evaluation methodology that makes that comparison honest. This is one mission of many; more will follow as we double-click into AI engineering.