Job description

We help the world run better At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. This a hybrid role based out of Palo Alto. Hybrid is 3 days a week onite, 2 days a week remote. The Office of the CTO (oCTO) for IndustryA I sits at the intersection of the SAP Business AI Platform (BAIP), Industry Product & Engineering, the LoB application teams, and the FDE engagements with strategic customers. The Engineering team inside oCTO does not own product roadmaps for any single industry or LoB that lives with the product and engineering organizations. What we build is the AI-native and agentic software that turns the IndustryAI compounding loop into working systems. The pipelines that ingest customer data into ontologies and knowledge graphs, the harnesses that train and evaluate decision and foundation models, and the agents the IAI Data Labs and Agent Factory ship into production. If a customer trusts an agent to act on their operations, it is because someone built it to be correct, fast, and observable. As Principal AI Developer , you are the senior hands-on engineer who builds that software end-to-end. You own the design and implementation of complex AI-native and agentic systems within one or more capability areas. You set the engineering bar through the code you write and the reviews you run, and you turn ambiguous problems into shipped, production-grade capabilities. This is a deep individual-contributor role: you write the hardest code, you make the technical calls on one's own, and you are the engineer other engineers escalate to — while raising the craft of the developers around you. In this role you will: • Design and build AI-native and agentic systems end-to-end. From data ingestion and knowledge-graph construction through model integration, agent orchestration, and production serving. As the senior implementing engineer, not just the reviewer. • Own the hardest engineering problems in your capability area: decompose ambiguous requirements into sound designs, prototype quickly, and drive them through to production-grade, well-tested code. • Build the agentic layer. Implement multi-agent orchestration, tool and function calling, retrieval-augmented generation (RAG), context, and memory engineering. As well as the guardrails that keep agents safe and reliable in production. • Integrate LLMs and ML models into enterprise systems — inference infrastructure, prompt and context pipelines, evaluation harnesses, and observability — grounded in what actually ships reliably at scale. • Practice specification-driven engineering. Write precise technical specs, validate designs against architectural guardrails. Hold the quality bar through implementation and code review. • Build the evaluation and observability that make AI systems credibility: eval suites, trace capture, regression harnesses, and the automated tests that catch drift and behavioral regressions before customers do. • Turn one-off solutions from FDE engagements into reusable, documented libraries and services that other IndustryAI engineering teams can adopt — code the whole organization builds on. • Feed the compounding loop from the engineering side: ensure production signal, traces, and incident learnings flow back into the harness, the eval suites, and the codebase so the systems get better between releases. • Contribute concrete requirements to BAIP. Orchestration primitives, memory and context APIs, inference tooling, evaluation harnesses, and observability hooks — grounded in what you need to build consistently. • Set the engineering standard through practice: CI/CD, MLOps, testing discipline, and responsible-AI practices embedded in how the team ships, not bolted on afterward. • Mentor senior and mid-level engineers on implementation, design tradeoffs, and code quality — multiplying your impact through the engineers you make better. • Partner with architects, data scientists, and product management to translate objectives into working systems with clear, measurable behavior. What you'll bring: • 7–10 years of professional software-engineering experience, preferably in cloud or enterprise SaaS, with meaningful time building AI/ML or data-intensive systems. • Deep, current hands-on expertise building production software in AI-native systems — you write the code, and you are exemplary at it. • Demonstrated experience shipping enterprise-scale systems end-to-end. Distributed systems, cloud-native services, and ML or data platforms in production, not prototypes. • Practical experience building agentic and LLM-powered systems in production. Multi-agent orchestration, tool/function calling, RAG, context and memory engineering, and evaluation harness design. • Solid software-engineering fundamentals: clean design, testing discipline, performance and reliability instincts, and the judgment to know when to prototype fast and when to harden. • Fluency in cloud-native MLOps: CI/CD pipelines, model registries, feature stores, inference serving, observability stacks, and infrastructure as code. • Experience integrating models and agents against enterprise constraints — security, data governance, and compliance designed in from the start. • Comfort with specification-led engineering: the ability to decompose ambiguous problems, write precise specs, and hold the bar during implementation and review. • Exemplary technical communication: able to make design tradeoffs legible to architects, product, and fellow engineers alike. • Proficiency in one or more languages used for AI systems engineering (e.g., Python, TypeScript, Go, Java) and the modern AI/ML toolchain. • Bachelor's degree in Computer Science, Software Engineering, or a related discipline; advanced degree is a plus. • Domain fluency in one or more SAP-relevant areas (supply chain, manufacturing, asset management, procurement) is a plus. Meet your team: The Office of the CTO for IndustryAI is where SAP's AI strategy comes to life. We connect the SAP Business AI Platform (BAIP) to the industry vertical requirements and LoB applications customers use every day, and we pair that with a Forward Deployed Engineering muscle that ships AI outcomes with strategic customers. The systems we build are grounded in those engagements; the platform we shape exists to make what we build scale. The Engineering team inside oCTO builds the AI-native and agentic software behind the IndustryAI compounding loop. The IAI Data Labs pipeline that turns customer data into ontologies, knowledge graphs, and decision and foundation models, and the Agent Factory that runs the resulting agents in test and production. Product roadmaps for any given industry stay with the product and engineering organizations. Our job is to build the systems, libraries, and reference implementations they ship on top of — code that is correct, observable, and meaningfully better between releases. You will join a small team of senior AI engineers inside oCTO IndustryAI who pair deep hands-on craft with systems-level thinking and the clarity to build across engineering. Bring out your best SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best. We win with inclusion SAP’s culture of inclusion, focus on health and well-being, and flexible working models help ensure that everyone – regardless of background – feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world. SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e-mail with your request to Recruiting Operations Team: Careers@sap.com. For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program , according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training. Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, age, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability. Compensation Range Transparency : SAP believes the value of pay transparency contributes towards an honest and supportive culture and is a significant step toward demonstrating SAP’s commitment to pay equity. SAP provides the annualized compensation range inclusive of base salary and variable incentive target for the career level applicable to the posted role. The targeted annual combined range for this position is 198200-420000USD . The actual amount to be offered to the successful candidate will be within that range, dependent upon the key aspects of each case which may include education, skills, experience, scope of the role, location, etc. as determined through the selection process. Any SAP variable incentive includes a targeted dollar amount and any actual payout amount is dependent on company and personal performance. Please reference this link for a summary of SAP benefits and eligibility requirements: SAP North America Benefits . AI Usage in the Recruitment Process For information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process . Please note that any violation of these guidelines may result in disqualification from the hiring process. Requisition ID: 459308 | Work Area: Software-Design and Development | Expected Travel: 0 - 10% | Career Status: Professional | Employment Type: Regular Full Time | Additional Locations: #LI-Hybrid

Requirements

Must-haves

We help the world run better
This a hybrid role based out of Palo Alto. Hybrid is 3 days a week onite, 2 days a week remote.
Own the hardest engineering problems in your capability area: decompose ambiguous requirements into sound designs, prototype quickly, and drive them through to production-grade, well-tested code.
Integrate LLMs and ML models into enterprise systems — inference infrastructure, prompt and context pipelines, evaluation harnesses, and observability — grounded in what actually ships reliably at scale.
Practice specification-driven engineering. Write precise technical specs, validate designs against architectural guardrails. Hold the quality bar through implementation and code review.
Build the evaluation and observability that make AI systems credibility: eval suites, trace capture, regression harnesses, and the automated tests that catch drift and behavioral regressions before customers do.
Turn one-off solutions from FDE engagements into reusable, documented libraries and services that other IndustryAI engineering teams can adopt — code the whole organization builds on.
Feed the compounding loop from the engineering side: ensure production signal, traces, and incident learnings flow back into the harness, the eval suites, and the codebase so the systems get better between releases.
Contribute concrete requirements to BAIP. Orchestration primitives, memory and context APIs, inference tooling, evaluation harnesses, and observability hooks — grounded in what you need to build consistently.
Set the engineering standard through practice: CI/CD, MLOps, testing discipline, and responsible-AI practices embedded in how the team ships, not bolted on afterward.
Mentor senior and mid-level engineers on implementation, design tradeoffs, and code quality — multiplying your impact through the engineers you make better.
Partner with architects, data scientists, and product management to translate objectives into working systems with clear, measurable behavior.
7–10 years of professional software-engineering experience, preferably in cloud or enterprise SaaS, with meaningful time building AI/ML or data-intensive systems.
Deep, current hands-on expertise building production software in AI-native systems — you write the code, and you are exemplary at it.
Demonstrated experience shipping enterprise-scale systems end-to-end. Distributed systems, cloud-native services, and ML or data platforms in production, not prototypes.
Practical experience building agentic and LLM-powered systems in production. Multi-agent orchestration, tool/function calling, RAG, context and memory engineering, and evaluation harness design.
Solid software-engineering fundamentals: clean design, testing discipline, performance and reliability instincts, and the judgment to know when to prototype fast and when to harden.
Fluency in cloud-native MLOps: CI/CD pipelines, model registries, feature stores, inference serving, observability stacks, and infrastructure as code.
Experience integrating models and agents against enterprise constraints — security, data governance, and compliance designed in from the start.
Comfort with specification-led engineering: the ability to decompose ambiguous problems, write precise specs, and hold the bar during implementation and review.
Exemplary technical communication: able to make design tradeoffs legible to architects, product, and fellow engineers alike.
Proficiency in one or more languages used for AI systems engineering (e.g., Python, TypeScript, Go, Java) and the modern AI/ML toolchain.

Nice-to-haves

Bachelor's degree in Computer Science, Software Engineering, or a related discipline; advanced degree is a plus.
Domain fluency in one or more SAP-relevant areas (supply chain, manufacturing, asset management, procurement) is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlci/cdllmpythontypescriptgojavaaccessibility

Platform: successfactors
Effort: medium