DevOps Engineer
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