AI Tech lead

About the job

KPMG Israel is more than a professional services firm, we are a community of people driven by impact.

As part of a leading global network, we partner with the most influential organizations to drive large-scale business and technology transformation, combining strategic thinking, innovation, and advanced technologies.

Guided by integrity, excellence, and forward thinking, we help organizations navigate complexity and create real change. 

Our people are at the center, with space to lead, experiment, and grow in a dynamic and collaborative environment that connects personal development with meaningful impact on the future of business and society.

About the Role

We're looking for a hands-on Generative AI Tech lead to both build and guide. You'll architect and ship production GenAI systems while mentoring a team of engineers - keeping one hand on the keyboard and one eye on the roadmap. This isn't a seat at a whiteboard; it's a role for someone who can write a RAG pipeline in the morning and run a technical design review in the afternoon.

What you'll do

Lead the design and delivery of end-to-end GenAI solutions, from prototyping to production - LLM-powered applications, agents, RAG systems, and fine-tuned models.

Stay hands-on: write production-quality code, review PRs, and set the technical bar for the team.

Define architecture patterns for prompt engineering, context management, retrieval pipelines, and model evaluation.

Mentor and grow a team of AI/ML engineers -run design reviews, pair-program, and provide technical feedback.

Partner with product, data, and platform teams to scope feasibility and drive AI initiatives from ideation to deployment.

Evaluate and introduce new models, tools, and frameworks (LangChain, LlamaIndex, vector DBs, etc.) as the space evolves

Requirements

3+ years of software engineering, with 2+ years in ML/AI roles

Hands-on experience building and shipping LLM-based applications (RAG, agents, fine-tuning)

Proficiency in Python; strong command of ML/AI libraries

Experience with at least one major LLM provider (OpenAI, Anthropic, Cohere, open-source)

Proven ability to lead or mentor engineers in a technical context

Comfort working in cloud environments (AWS, GCP, or Azure) with MLOps practices