TL;DR
Senior ML Infrastructure Engineer (AI): Designing and building robust infrastructure and backend services for production-grade ML and LLM systems with an accent on scalability, reliability, and reproducibility. Focus on building and optimizing deployment pipelines, tooling for model versioning, and ensuring the production-readiness of research prototypes to improve behavioral health care quality.
Location: Tel Aviv District, Israel
Company
Eleos Health develops a behavioral health CareOps automation platform that uses advanced AI to convert therapy conversations into actionable clinical insights.
What you will do
- Design and maintain production infrastructure supporting scalable ML and LLM systems.
- Develop and manage automated training and deployment pipelines for machine learning models.
- Build internal tooling for model experimentation, versioning, and reproducibility.
- Implement CI/CD workflows tailored for ML and model deployment.
- Ensure observability, reliability, and monitoring of AI systems in a production environment.
- Collaborate with data scientists to bridge the gap between research prototypes and scalable production systems.
Requirements
- 5+ years of industry experience in ML Infrastructure or Backend Engineering.
- Strong proficiency in Python development for production systems.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure).
- Experience with containerization tools like Docker and Kubernetes.
- Proven track record of building CI/CD pipelines for backend or ML services.
- Experience managing LLM deployment or ML models in production environments.
Nice to have
- Experience with prompt engineering and LLM lifecycle management.
- Familiarity with data versioning tools like DVC or Pachyderm.
- Hands-on knowledge of MLOps platforms such as MLflow or Kubeflow.