Customer-Facing AI Engineer for Enterprise Solutions

SAP SE; New York (NY); Hybrid; USD 90,000 - 198,500; Full time

Job summary

SAP SE seeks an experienced Forward Deployed AI Engineer to build AI-native enterprise applications for customers in a hybrid New York City-based role. You will own technical workstreams, design production-grade AI solutions, and collaborate with architects to deliver scalable, secure, enterprise-ready AI apps.

3–7 years of experience with Generative AI and cloud-native engineering is preferred. Responsibilities include building AI agents using LLMs, RAG, MCP, tool calling, and orchestrating

Qualifications

• 3-7 years of professional software engineering experience.
• Hands-on experience with AI-native enterprise applications.
• Experience building REST APIs, microservices, and cloud-native apps.

Responsibilities

• 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 apps, 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.

Skills

Software engineering

AI engineering

LLMs

REST APIs

Docker

Kubernetes

Cloud

Git

Education

Bachelor's or Master's in Computer Science

Tools

LangGraph

LangChain

CrewAI

Semantic Kernel

AutoGen

Job description

SAP SE seeks an experienced Forward Deployed AI Engineer to build AI-native enterprise applications for customers in a hybrid New York City-based role. You will own technical workstreams, design production-grade AI solutions, and collaborate with architects to deliver scalable, secure, enterprise-ready AI apps.

3–7 years of experience with Generative AI and cloud-native engineering is preferred. Responsibilities include building AI agents using LLMs, RAG, MCP, tool calling, and orchestrating