Dream is a nation-level platform that combines AI and cybersecurity to protect critical assets from existing cyber attacks and future generative cyber threats. Our disruptive approach to processing diverse cyber data turns it into clear and meaningful narratives. Using advanced machine learning capabilities to uncover hidden insights and bridge the gap between complex cyber activities.

With our advanced generative AI capabilities, we enable seamless automated network mapping, improving risk management and threat visibility for critical organizations at the national level.

We are searching for an experienced MLOps Engineer to join our DevOps team within our Engineering group. Our ideal candidate is passionate about software design, development, and deployment, as the role entails crafting production-grade modern DevOps solutions destined for both cloud and on-premises deployment.

Responsibilities:

• Design, develop, build and maintain the best solutions for our production platform.
• Everything as a code approach (IaC): Run our infrastructure with a wide range of technologies including Ansible, Terraform, and Kubernetes
• Work closely with our data scientists and developers to create training, inference and serving pipelines.
• Build and maintain tools for automation, deployment, monitoring, and operations.
• Troubleshoot issues in our development, production, and test environments
• Excellent communication and people skills
• Work well in a team!

Skills:

• At least 4-5 years experience in one of the following roles: DevOps, MLOps.
• Experience with design, build, development and maintenance of DevOps solutions.
• Experience with one of the major cloud providers: AWS, GCP, Azure.
• Experience Working cloud & on-prem environments and solutions.
• Solid Linux system expert skills - a must
• Vast Experience with applications and tooling including Kubernetes, Helm, Terraform, Ansible, SQL\NoSQL\Graph DBs, MLFlow, Jenkins, GitHub, etc.
• Experienced with CI\CD technologies.
• Experience with bootstrapping projects, introducing new technologies and building systems from scratch.
• Good coding capabilities (python\bash etc.)

Advantages:

• Experience working on endpoint products (agent/sensors/collectors)
• Experience working on AI components (Training, inference, serving)

Tech stack:

• AWS, Kubernetes, KubeFlow, SageMaker, EKS, ECS, Jenkins, IaC, GitHub, Terraform, Python, Ansible, Docker+Compose, ArgoCD, MongoDB, RabbitMQ, Redis, Go, Neo4J, AI, MLFlow, Clickhouse, Jupyter and more.