How do I apply for the AI Developer - Analytics & Agentic AI - F/M position at SAP and track my application?

We provide a direct link to the official application portal. You can apply directly here . Additionally, you can easily track this and other applications by clicking the bookmark icon to save it to your YGI Kanban board .

Is the internship at SAP paid and what compensation can candidates expect?

Yes, this is a paid internship opportunity. Exact compensation details are typically finalized during the company interview process.

What are the working arrangements and expected duration for the AI Developer - Analytics & Agentic AI - F/M role?

This position is a full-time, structured as a hybrid role, with an expected duration of 6 months.

What educational background or degree level is required for the AI Developer - Analytics & Agentic AI - F/M at SAP?

Candidates are generally expected to be holding or pursuing the following degree levels: Master.

Does SAP provide visa sponsorship or assistance for international applicants?

Regarding visa assistance and sponsorship for this role, the official status is currently listed as: Not disclosed.

AI Developer - Analytics & Agentic AI - F/M

SAP

Found 2 months ago

Location

Levallois-Perret, France

Duration (Months)

6 Months

Time

Full-time

Work Mode

Hybrid

Salary

Paid in line with the SAP gratification grid

Visa Help

Not disclosed

Last Verified

1 month ago

Education

Master

Skills & Qualifications

Technical Skills

Python
FastAPI
Flask
JavaScript/TypeScript
ML/Deep Learning
LLMs
transformer models
REST APIs
Django
Git/GitHub
Docker
Linux
Hugging Face
OpenAI
RAG
vector databases
ChromaDB
Pinecone
Milvus
FAISS
embedding models
MLOps/LLMOps
MLflow
BentoML
LangChain
langgraph
Kubernetes

Soft Skills

team player
takes initiative
perseveres
stay curious
collaborative software development workflows
collaborative agile teamwork
communication skills
personal development

Job Description

First, you’ll be onboarded within the team to understand the SAP Business Data Cloud solution, our development culture and delivery process, then you’ll participate in the AI-based alternative of our Line of Business Query Designer.

Requirements

Prototype and implement AI components: LLM-powered pipelines, RAG, tool/agent integrations, and evaluation harnesses.
Implement backend services and APIs to expose AI capabilities (FastAPI/Flask or similar).
Develop and maintain CI/CD and MLOps pipelines to move PoCs to repeatable deployments (Jenkins, GitHub Actions, etc.).
Containerize and deploy components using Docker and Kubernetes (or local dev equivalents).
Help design, run, and analyze automated tests and evaluation metrics for model quality, hallucination, latency, and cost.
Collaborate with data engineers to build and maintain retrieval indices, embeddings pipelines, and feature stores.
Participate in design, sprint reviews, and cross-functional demos with product, QA, and UX.
Contribute to documentation, reproducible experiments, and internal enablement material.
Master’s student in Computer Science, Engineering, Data Science, Artificial Intelligence or related field.
Strong Python programming skills; familiarity with JavaScript/TypeScript is a plus.
Experience or coursework with ML/Deep Learning fundamentals and at least basic exposure to LLMs / transformer models.
Familiarity with REST APIs and backend frameworks (FastAPI, Flask, Django).
Experience with Git/GitHub and collaborative software development workflows.
Basic knowledge of containers (Docker) and comfort using Linux and cloud services.
Interest in productionizing ML systems, good software engineering hygiene, and collaborative agile teamwork.
Excellent communication skills in English and French (at least B2 level in both)

AI & Machine Learning

Applied Machine Learning

Languages

English
French

Nice to Haves

Hands-on experience with Hugging Face, OpenAI, or other LLM platforms; prompt engineering and evaluation experience.
Familiarity with RAG approaches, vector databases (e.g., ChromaDB, Pinecone, Milvus or FAISS), and embedding models.
Exposure to MLOps/LLMOps tooling (MLflow, BentoML), observability, and cost-aware deployment.
Experience with agentic frameworks (LangChain, langgraph) or multi-tool orchestrations.
Knowledge of data engineering, Kubernetes, and cloud platforms.

Apply Now

9Open Positions