Cutting-edge AI startup seeking a Lead MLOps Platform Developer for our global foundation team.
About the Role:
Lead the development and maintenance of our MLOps platform, supporting advanced ML research and deploying large-scale ML models and LLM agents.
Responsibilities:
Design and build cloud infrastructure for the entire ML lifecycle.
Deploy and optimize classical ML models and LLM agents.
Manage LLM agent operations (context, tools, prompts).
Implement CI/CD pipelines and monitoring.
Use IaC tools (Terraform, CloudFormation).
Provide technical leadership.
About the Role:
Lead the development and maintenance of our MLOps platform, supporting advanced ML research and deploying large-scale ML models and LLM agents.
Responsibilities:
Design and build cloud infrastructure for the entire ML lifecycle.
Deploy and optimize classical ML models and LLM agents.
Manage LLM agent operations (context, tools, prompts).
Implement CI/CD pipelines and monitoring.
Use IaC tools (Terraform, CloudFormation).
Provide technical leadership.
Location Travel:
Remote (Israel).
Occasional travel to Paris (4 days/month)
Requirements:
6+ years of experience in MLOps, infrastructure development, or a similar field.
Proven experience in developing and deploying both classical ML models and LLM agents in production environments.
Strong proficiency in Python and Bash scripting.
Extensive experience with ML model deployment tools such as MLflow, Weights Biases / Vertex / SageMaker and DVC.
Deep expertise in cloud platforms (AWS, GCP, Azure), Infrastructure as Code (IaC) tools (Terraform, CloudFormation), CI/CD, containerization (Docker, Kubernetes), and monitoring/observability tools like Grafana.
Strong understanding of LLM agent operations, including context management, tool integration, and prompt engineering.
Excellent problem-solving and debugging skills.
Strong communication and collaboration skills.
Experience leading or mentoring a team is highly desirable
Skills
6+ years of experience in MLOps, infrastructure development, or a similar field.
Proven experience in developing and deploying both classical ML models and LLM agents in production environments.
Strong proficiency in Python and Bash scripting.
Extensive experience with ML model deployment tools such as MLflow, Weights Biases / Vertex / SageMaker and DVC.
Deep expertise in cloud platforms (AWS, GCP, Azure), Infrastructure as Code (IaC) tools (Terraform, CloudFormation), CI/CD, containerization (Docker, Kubernetes), and monitoring/observability tools like Grafana.
Strong understanding of LLM agent operations, including context management, tool integration, and prompt engineering.
Excellent problem-solving and debugging skills.
Strong communication and collaboration skills.
Experience leading or mentoring a team is highly desirable
Skills
This position is open to all candidates.
