GenAI Backend Engineer

Description

About the Team

At KPMG Israel, our Generative AI delivery team builds end-to-end AI-powered products for clients across diverse industries. We combine strong software engineering with GenAI capabilities from multi-agent architectures to complete production systems to deliver solutions that create real business value.

About the Role

We're looking for a Backend Engineer with a data science foundation to join our AI team and build production systems that integrate Generative AI capabilities. You'll design and implement end-to-end solutions APIs, databases, orchestration, and cloud infrastructure for clients across a wide range of sectors.

This role is ideal for someone who thinks in systems, enjoys working with GenAI technologies, and wants to grow into designing complex architectures. You'll work alongside senior engineers on real delivery projects from day one.

Key Responsibilities

· Design and build backend systems APIs, databases, authentication, and integrations

· Implement multi-agent architectures and orchestration workflows combining LLMs with backend logic

· Deploy and maintain systems on GCP, Azure, or AWS

· Optimize AI system performance, cost, and reliability

· Collaborate with client teams, DevOps, and stakeholders on project delivery

· Contribute to technical discussions and solution design

Requirements

Must Have

· 1+ years of software development experience

· Strong proficiency in Python for backend development

· Experience building backend systems (APIs, databases, or services)

· Solid understanding of data science fundamentals statistics, ML concepts, and working with data pipelines

· Familiarity with at least one major cloud platform (GCP, Azure, or AWS)

· Understanding of software engineering principles and architecture basics

· Familiarity with GenAI / LLM concepts

· Bachelor's degree in Computer Science, Data Science, or related field or equivalent practical experience

Advantages

· Experience with LLM provider APIs (OpenAI, Anthropic, Google)

· Exposure to multi-agent systems or LLM-based workflows

· Hands-on experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow)

· Academic research or projects in AI, NLP, or data science

· Personal or university projects involving multi-agent systems or ML model development

· Docker and Kubernetes experience

· Experience working on production systems in a client-facing environment

What We Offer

· Hands-on work designing end-to-end AI systems across multiple industries

· Exposure to diverse cloud platforms and technical challenges

· Mentorship and a clear path to grow into system design and technical leadership

· Continuous learning in a rapidly evolving field