לוח משרות דרושים NLP/Machine Learning בנתניה

Are you a software engineer who thrives on designing and building scalable, high-quality services and infrastructure? Do you want to work on cutting-edge services while shaping the backbone of an ML platform? If so, wed love to have you join our company ML.
As a Senior Software Engineer, you will play a critical role in architecting and developing cloud based core services and infrastructure with a strong emphasis on clean code, performance, observability, monitoring, and security. This role also includes developing core Go/Java libraries, cross-cutting services like authentication and authorization, Kubernetes deployments, billing etc. You will collaborate closely with engineering teams, DevOps, architects, and product managers to ensure a scalable, stable, and high-performing system.
As a Senior Software Engineer at our company ML you will...
Write clean, maintainable, and efficient code following best practices.
Design and develop robust backend services with scalability, performance, and security in mind.
Conduct high-quality code reviews and architecture discussions, ensuring best practices are followed.
Take full ownership of projects - from ideation and design to production deployment and maintenance.
Collaborate with cross-functional teams to define, design, and ship new features.
Stay current with industry trends, technologies, and best practices in software development and cybersecurity.
Be involved in multiple aspects of an ML platform - from data sources to inference pipelines.

We are seeking a highly skilled and motivated MLOps Engineer to join our team and play a critical role in the deployment, automation, and maintenance of our Machine Learning infrastructure. You will be responsible for ensuring that our ML models move seamlessly from experimentation to production with stability, security, and scalability in mind. You will work closely with data scientists, Machine Learning engineers, and DevOps teams to build robust model pipelines, manage infrastructure across on-prem and cloud, and enforce best practices in version control, model deployment, and compliance. Key Responsibilities: Design, implement, and maintain automated model training pipelines using tools such as MLflow, (Kubeflow, Airflow), or custom orchestrators. Support reproducibility and consistency across model training environments. Implement and manage Artifactory. Establish and maintain model registries to track versions, metadata, and lineage. Automate model promotion through staging, testing, and production environments. Build CI/CD pipelines tailored for ML use cases, integrating model training, validation, deployment, and rollback. Manage and scale infrastructure across cloud platforms (Azure) and on-premise environments Optimize GPU/CPU resource utilization and cost efficiency. Implement auto-scaling and load balancing strategies for ML workloads. Manage version control systems (e.g., Git) and integrate with experiment tracking tools. Handle Storage and retrieval of artifacts (e.g., Docker images, models, datasets) via artifact registries like JFrog Artifactory or AWS ECR.

We are seeking a highly skilled and motivated MLOps Engineer to join our team and play a critical role in the deployment, automation, and maintenance of our machine learning infrastructure. You will be responsible for ensuring that our ML models move seamlessly from experimentation to production with stability, security, and scalability in mind.
You will work closely with data scientists, machine learning engineers, and DevOps teams to build robust model pipelines, manage infrastructure across on-prem and cloud, and enforce best practices in version control, model deployment, and compliance.
Key Responsibilities:
Design, implement, and maintain automated model training pipelines using tools such as MLflow, (Kubeflow, Airflow), or custom orchestrators.
Support reproducibility and consistency across model training environments.
Implement and manage Artifactory.
Establish and maintain model registries to track versions, metadata, and lineage.
Automate model promotion through staging, testing, and production environments.
Build CI/CD pipelines tailored for ML use cases, integrating model training, validation, deployment, and rollback.
Manage and scale infrastructure across cloud platforms (Azure) and on-premise environments.
Optimize GPU/CPU resource utilization and cost efficiency.
Implement auto-scaling and load balancing strategies for ML workloads.
Manage version control systems (e.g., Git) and integrate with experiment tracking tools.
Handle storage and retrieval of artifacts (e.g., Docker images, models, datasets) via artifact registries like JFrog Artifactory or AWS ECR.