Machine Learning Engineer

One of our clients, an early-stage deep tech startup, is hiring an MLOps Engineer to join their core engineering team. The company is building next-generation AI infrastructure for large-scale structured data applications, operating at the intersection of research and real-world impact.

Key Responsibilities:

• Build and maintain scalable ML pipelines and cloud-based training environments.
• Develop high-performance inference systems with ultra-low latency.
• Lead CI/CD, A/B testing, and model deployment processes.
• Optimize GPU usage, autoscaling, and system monitoring.
• Design feature stores and robust data pipelines.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
• 5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)..
• Experience building and designing MLOps infrastructure from the ground up.
• Proven ability to deploy machine learning models into a production environment, ensuring high scalability and low latency inference.
• Experience in building and managing data pipelines to support both model training and inference.
• Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps).
• Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code.
• Experience in AI/ML systems security, compliance, and model governance.
• Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry.

Company location: TLV (remote-friendly) - 4 days from home

This position reports to: Head of Engineering.