Machine Learning Engineer

We’re looking for a Machine Learning Engineer to join our client’s R&D team and help bring cutting-edge ML solutions into production.
You’ll take full ownership of the ML lifecycle, from data and feature engineering, through training and deployment, to real-time monitoring in the cloud.
 

Requirements

• 4+ years of experience deploying ML models (classical and/or LLM) in cloud environments (AWS / GCP / Azure) using containers and Kubernetes
• Practical experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, Feast, or similar)
• Strong background in building and maintaining robust data and feature pipelines
• Experience implementing real-time model monitoring (drift, latency, performance)
• Solid software engineering skills version control, testing, CI/CD, security, and cost optimization
• Excellent communication skills in English (Upper-Intermediate or higher); Hebrew is a plus
• Ownership mindset, curiosity, and proactive approach in a fast-moving environment

Nice to have:

• Experience with deep learning frameworks (PyTorch, TensorFlow)
• Familiarity with distributed systems (Spark, Ray)

• Knowledge of feature stores and data governance

   

Responsibilities

• Design, build, and maintain end-to-end ML pipelines for both classical and LLM-based models
• Deploy and manage ML solutions in cloud environments (AWS/GCP/Azure) using Docker and Kubernetes
• Apply MLOps best practices for reproducibility, monitoring, and automation
• Build and maintain robust data pipelines and feature stores
• Implement real-time monitoring for model drift, accuracy, and performance
• Collaborate with cross-functional teams to integrate ML components into production systems
• Ensure engineering best practices CI/CD, automated testing, cost optimization, and security