תיאור המשרה
Description
Jeen.AI empowers enterprises with generative AI through advanced AI agents, automations, voice analytics, and knowledge-based insights across any cloud or on-premise environment. Trusted by industry leaders shaping tomorrow's technological landscape.
Why Join us?
This role includes hands-on work with government, defense, and security organizations in Israel and across global markets.
Take part in building cutting-edge AI infrastructure from the ground up.
As a DevOps Engineer, you'll play a key role in deploying our platform directly at customer sites, influencing implementation across diverse environments.
This is a unique opportunity to take ownership of complex, hands-on deployments, work across multiple cloud providers, and lead system automation in real-world production scenarios.
Join us to tackle real technical challenges and grow in a fast-moving, high-impact environment.
Responsibilities
Installing and deploying our platform in customer environments
Independently determine and develop architectural approaches and solutions
Build automated deployment and testing environments
Manage performance bottlenecks and continuously improve the security and resilience of our platform
Act as a knowledge source for other team members
Management horizon for team leadership
Work with cloud platforms such as GCP, Azure, or AWS
Use scripting and automation tools to streamline infrastructure processes
Requirements
At least 3 years of experience as a DevOps Engineer
Proven hands-on experience with microservices. Docker, Kubernetes, and OpenShift
Experience working in a multi-cloud environment (AWS / GCP / Azure)
Knowledge of build/release systems, CI/CD pipelines (GitHub Actions/Jenkins)
Scripting/programming skills with Python/Bash
Experience with PostgresDB
Understanding of monitoring tools such as Grafana, Prometheus, and other monitoring technologies
High problem-solving skills, along with the ability to work independently
Preferred Qualifications
Security Clearance
Experience with PostgresDB
Familiarity with monitoring technologies
Background in AI/ML infrastructure
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