Forward Deployed AI Engineer
Posted 2 days ago
The role in short
Build and deploy production-grade AI applications and agents for enterprise customers using SAP technologies and modern AI frameworks.
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.
Location: this is a hybrid position based from our New York City office located in the Hudson Yards.
What you'll build
As a Forward Deployed AI Engineer, you will independently build AI-native enterprise applications and work directly with customers to solve complex business problems. You will own technical workstreams, design production-grade AI solutions, and collaborate with architects to deliver scalable, secure, enterprise-ready AI applications. This role is designed for engineers with 3–7 years of experience who are passionate about Generative AI, cloud-native engineering, and customer success.
In this role, you will:
- Design and implement production-grade AI applications using modern software engineering practices.
- Develop AI agents using LLMs, RAG, MCP, tool calling, structured outputs, and multi-agent orchestration.
- Build integrations with SAP products, enterprise applications, APIs, databases, and external services.
- Deploy and operate AI applications on SAP AI Core, SAP BTP, Kubernetes, and public cloud platforms.
- Own customer-facing technical workstreams and translate business requirements into working AI solutions.
- Develop reusable frameworks, accelerators, and reference implementations.
- Perform code reviews and mentor Associate AI Developers.
- Optimize AI systems for performance, reliability, observability, and cost.
- Collaborate with architects, product engineering, and customer stakeholders.
- Drive innovation through prototypes and emerging AI technologies.
What you bring
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Information Technology, or a related discipline.
- 3–7 years of professional software engineering experience.
- Strong Python programming skills with experience in Java, TypeScript, or JavaScript.
- Hands-on experience with LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, or similar frameworks.
- Strong understanding of LLMs, prompt engineering, embeddings, vector databases, RAG, MCP, and agentic AI.
- Experience building REST APIs, microservices, and cloud-native applications.
- Hands-on experience with Docker, Kubernetes, Git, CI/CD, and AWS, Azure, or GCP.
- Experience integrating enterprise applications and databases.
- Exposure to SAP BTP, SAP AI Core, Joule, or SAP Business AI services is highly desirable.
- Excellent communication, analytical thinking, ownership, and customer engagement skills.
Nice to have
- Experience with Knowledge Graphs, GraphRAG, AI evaluation, and AI observability.
- Experience delivering enterprise AI projects in customer-facing roles.
- Open-source contributions, hackathons, or technical community involvement.
- Experience using GitHub Copilot, Cursor, Claude Code, or similar AI-assisted engineering tools.
#SAPIndustryAICareers #SAPAICareers
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 90000-198500USD. 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.
Prep for this interview
5 questions likely to come up at SAP, each matched to a line in this posting.
- 1Cloud, deployment and security
They wrote: “Hands-on experience with Docker, Kubernetes, Git, CI/CD, and AWS, Azure, or GCP.”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
- 2LLMs, RAG and agents
They wrote: “Strong understanding of LLMs, prompt engineering, embeddings, vector databases, RAG, MCP, and agentic AI.”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 3Coding
They wrote: “Strong Python programming skills with experience in Java, TypeScript, or JavaScript.”
Here's a CSV export from a customer. Write a script that cleans it and loads it into our API.
How to answer
Why they ask: Very close to real FDE work. They watch how you handle bad rows and failures.
- Read with a proper CSV parser; check the header first
- Validate each row and collect errors, not crash on the first one
- Send in batches with retries and a clear report at the end
- Write one or two quick tests for the parsing
Tip: Talk through edge cases out loud: encodings, missing fields, duplicates, rate limits.
- 4Integrations and APIs
They wrote: “Build integrations with SAP products, enterprise applications, APIs, databases, and external services.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 5Discovery and scoping
They wrote: “Own customer-facing technical workstreams and translate business requirements into working AI solutions.”
Tell me about a time the customer asked for one thing but needed another.
How to answer
Why they ask: Good FDEs spot when a request is a guess at a solution. They want proof you can redirect a customer without losing their trust.
- What they asked for, and the detail that made you doubt it
- How you tested the doubt (shadowed users, looked at logs, ran a quick prototype)
- How you made the case to the customer
- What shipped and what changed for them
Tip: Pick a story where you checked your hunch with data or users before pushing back.
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