DevOps Engineer

Requirements:

• 5+ years in DevOps and software development
• Familiarity with common development tools like Git 
• Experience with Terraform 
• Scripting skills in Python
• Deep knowledge of Kubernetes
• Understanding CI/CD principals
• Familiarity with TCP/IP and Linux networking stack
• Experience with Helm
• Good written and verbal communication skills in English

Would be a plus:

• Experience with cloud providers like GCP, AWS, Azure to test cloud-agnostic solution
• Knowledge of ELK stack
• Experience with Keycloak
• Experience with GCP
• Experience with F5 Big IP
• Experience with Prometheus/Grafana

Responsibilities:

• Automate deployment of application infrastructure
• Manage CI/CD processes,
• Support of existing infrastructure
• Extend monitoring

About the project:

World leader in engines and propulsion systems and distributed power generation plants. The company develops and produces high-speed engines and propulsion systems for ships and heavy land, rail and defense vehicles, as well as drive systems for use in the oil and gas industry and in power generation.

The main area of the project: 

• Fleet management and engine performance monitoring; 
• Telematics tracking, geofencing, routing; 
• Fuel quality and consumption management; 
• Workload management and scheduling; 
• Predictive maintenance based on previously detected engine issues.

Problem: 

• The company is able to receive data from vessels, rail machinery, nuclear reactors, power plants, and mining equipment, but has issues with any further actions(transforming, processing, transferring) with that data.
• The main thing their solutions were doing - creating simple data reports and data visualization that basically just including the raw data along with building predefined charts for technical analysts.

Solution:

• We are building a cloud-agnostic solution that can be deployed to different cloud providers in different regions, also including deployment on a barebone hardware
• The platform computes live-streaming data that incoming from the sensors connected to the assets. When the platform gets the asset data, it’s visualizing and analyzing on the UI. 
• Predictive maintenance implemented with help of machine learning, configurable alerts, and thresholds.
• Configurable alerts can be set up with a wide system of rules and alert templates for faster and more efficient data analysis. 
• Our team responsible for the entire engineering cycle, from the initial device’s software design to ground-up implementation, testing automation, analytics, and UI production rollout.

Overall project technology stack:

Python, Java, Spring cloud, Oauth2, Gradle, Helm charts, Kubernetes, Typescript, Javascript, C, STOMP, DSL, FFT, Cloud providers like GCP, Azure, AWS