MLOps Engineer

We are seeking a versatile Backend & MLOps Engineer to join our Core AI Development Team. This team designs and builds tools that enable the firm to leverage Large Language Models (LLMs) in daily workflows, including enterprise Retrieval-Augmented Generation (RAG) systems and data ingestion pipelines. In this hybrid role, you will split your time between building robust backend systems in Python and managing the infrastructure, CI/CD pipelines, and observability platforms that keep our AI solutions running at scale.

Why Join Us

As a Backend & MLOps Engineer on the Core AI Development Team, you will occupy a unique and high-impact position — writing the backend code that powers our AI solutions and ensuring those solutions run reliably at scale. You will work on cutting-edge technologies, collaborate with talented colleagues, and directly influence the firm's ability to integrate AI into its operations. If you thrive at the intersection of software engineering and ML infrastructure, and you're excited about enabling innovation through both code and tooling, we encourage you to apply.

Our Israel office is located in the Bursa area of Ramat Gan.

This role will be on-site.

As a global firm, proficiency in English is required.

Responsibilities

• Design, implement, and maintain scalable build/release pipelines to support rapid development and deployment of ML-powered applications and APIs.
• Ensure the stability and scalability of our Kubernetes-based infrastructure supporting AI/LLM workloads.
• Contribute to the development of enterprise RAG solutions, data ingestion pipelines, and other AI-driven tools.
• Develop and manage metrics and monitoring systems leveraging DataDog to track system and model performance and reliability.
• Automate workflows and continuously improve CI/CD processes for ML-driven services.
• Troubleshoot and resolve infrastructure-related issues, ensuring minimal downtime for AI systems.
• Implement best practices for system security, reliability, and scalability in ML production environments.
  
  

• 5+ years of experience as a DevOps engineer, MLOps experience is preferred.
• Strong proficiency in Python for backend development, including building APIs and services.
• Solid experience with Kubernetes, including deployment, scaling, and troubleshooting.
• Proficiency with CI/CD tools and pipelines (e.g., Jenkins, GitLab CI/CD, GitHub Actions, or similar).
• Experience with monitoring and observability tools, particularly DataDog.
• Familiarity with infrastructure-as-code tools (e.g., Terraform, Helm).
• Experience with containerization technologies such as Docker.
• Proven ability to design and implement scalable and reliable systems.
  
  

• Experience with cloud platforms (AWS, GCP, Azure) for infrastructure management and application deployment.
• Familiarity with AI/LLM-related workflows, tools, and interfaces, including model serving and prompt management.
• Experience with data ingestion pipelines or event-driven architectures.