Subsurface Backend Developer
Luxoft View all jobs
- Calgary, AB
- Permanent
- Full-time
- Design, develop, and maintain backend code using Java, Python programming languages.
- Develop and manage RESTful APIs and microservices.
- Containerize applications using Docker and create/manage Kubernetes manifests and Helm charts for deployment.
- Deploy, manage, and scale applications within Kubernetes clusters on cloud platforms AWS, Azure, and customer's on-prem environments.
- Implement and manage CI/CD pipelines for automated builds, testing, and deployments.
- Design and interact with databases (SQL and NoSQL) ensuring optimal performance and data integrity.
- Monitor application and cluster health using tools like Prometheus, Grafana, and ELK stack; troubleshoot and resolve issues promptly.
- Implement and enforce security best practices for applications and infrastructure within the Kubernetes environment.
- Optimize applications for maximum speed, scalability, and resilience.
- Stay current with emerging cloud-native technologies and Kubernetes best practices.
- Contribute to infrastructure-as-code initiatives using tools like Terraform or Ansible.
- Participate in code reviews and advocate for high-quality coding standards.
- 5+ years in Java development
- BS or MS in Computer Science or related Engineering discipline
- Proven experience delivering software applications
- Solid understanding of how to build publicly exposed APIs
- Familiarity with CI/CD principles and tools technologies: Jenkins and GitLab Runner
- Must be self-motivated but be able to work well in a team environment
- Team player; willing to coach junior team members and cooperate on cross-functional problem solving
- Experience with Cloud and SaaS Development: Azure, AWS, Docker, Kubernetes.
- Working in an Agile development environment
- Knowledge of infrastructure-as-code tools (e.g., Terraform, Ansible). • Understanding of networking concepts in Kubernetes. • Experience with serverless computing. • Distributed Computing: distributed job orchestration on Kubernetes, parallel execution patterns, backpressure, sharding/partitioning, fault tolerance, checkpointing; frameworks (Ray/Spark/Dask) if applicable. We are mostly interested in Ray • Implemented Kafka-based message-driven services and event-driven autoscaling on Kubernetes using KEDA (consumer-group scaling, DLQ/error handling, and scaling policies with performance/cost guardrails).