LLM & Agentic Systems Engineer

At KPMG Israel, our Generative AI delivery team leads the market in advanced AI solution delivery. We work across the full spectrum of generative AI technologies, from large language models and multimodal architectures to autonomous agent systems and production-scale machine learning platforms. Our work combines advanced engineering with strategic advisory capabilities, delivering AI-driven solutions that create measurable business value for clients.

We are seeking an experienced LLM & Agentic Systems Engineer to join our AI squad at KPMG. This role blends deep hands-on engineering with architectural responsibility and client-facing advisory work. You will design, build, and operate production-grade LLM and multi-agent systems, working on both greenfield initiatives and the evolution of existing GenAI platforms. You will play a key role in shaping technical direction, best practices, and delivery standards across the GenAI practice.

Key Responsibilities

GenAI Development & Implementation

• Build end-to-end GenAI solutions from POC through production deployment
• Design and implement backend microservices architectures for GenAI applications using Python
• Design, implement, and maintain production-grade Python services with a focus on code quality, performance, and reliability
• Architect and develop multi-agent systems, orchestration layers, and autonomous workflows
• Integrate and optimize LLMs and GenAI APIs across complex systems
• Evaluate and improve system performance, scalability, reliabililty, and cost efficiency
  
  

• Lead technical discussions with clients and translate business needs into technical architectures
• Present GenAI solutions, design decisions, and trade-offs to technical and non-technical stakeholders
• Provide strategic technical guidance on GenAI adoption and system design
  
  

• Deploy and manage GenAI systems across GCP, Azure, and AWS
• Leverage cloud-native AI services (Vertex AI, Azure OpenAI, SageMaker, etc.)
• Own production environments, monitoring, and operational excellence
  
  

• Evaluate emerging GenAI models, frameworks, and techniques
• Define and refine best practices for GenAI system development and deployment
• Contribute to internal accelerators, methodologies, and knowledge sharing
  
  

• Advanced proficiency in Python for backend development and AI systems
• Deep understanding of large language models and generative AI techniques
• Hands-on experience designing and implementing multi-agent architectures
• Advanced prompt engineering and orchestration strategies
• Strong background in microservices architecture, API development, and production system design
• Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)
  
  

• 2–3+ years of experience in AI/ML development with significant GenAI project exposure
• Proven experience deploying and maintaining AI systems in production
• Client-facing experience in technical consulting or solution delivery roles
  
  

• Hands-on experience developing directly against LLM provider SDKs and APIs (e.g., OpenAI, Anthropic, Google), including tool/function calling, streaming, and advanced orchestration patterns.
• Docker and Kubernetes experience
• OCR systems and document intelligence experience
• Data pipeline development and maintenance experience
  
  

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related field (or equivalent demonstrated industry experience)
  
  

• Strong problem-solving and analytical capabilities
• Excellent technical communication skills
• Ability to collaborate effectively across teams
• Adaptability in fast-paced, evolving technical environments
• Consulting mindset with strong client focus
  
  

• Opportunity to lead cutting-edge GenAI projects across diverse industries
• Dual role combining hands-on engineering with strategic advisory work
• Access to advanced AI technologies and multi-cloud platforms
• Collaborative environment with cross-functional teams
• Ongoing professional growth in a rapidly evolving field