digitalgeopot31

Integrated GPU resources into Kubernetes clusters, preparing the environment for training and inference of neural network models. Set up end-to-end ML pipelines from data preparation to production predictions with automated triggering via GitLab CI/CD. Optimized GPU node utilization, customized the scheduler, and added extra metrics to ensure even workload distribution. Deployed containerized deep learning services with adaptive scaling to handle growing traffic. Segmented GPU usage between teams using RBAC, quotas, and namespace limitations. Designed and maintained complex AWS infrastructure: EKS, RDS, S3, CloudFront. Described the entire environment as IaC using Terraform and Helm, speeding up environment provisioning and onboarding of new engineers. Implemented an advanced CI/CD architecture (GitLab CI/CD) with automated tests and blue/green releases, making deployments safe and without downtime. Built a complete observability stack (Prometheus, Loki, Grafana, Alertmanager), reducing the number of critical incidents by almost half. Optimized AWS costs by properly configuring resources, achieving around 20% savings. Reduced the full release pipeline duration from 40 minutes to 8 minutes. Deployed and maintained a production Kubernetes cluster with automatic service self-healing. Created automated server configuration procedures with Ansible, minimizing manual operations. Administered MongoDB: replication, performance monitoring, and backup strategies. Introduced Redis as a high-speed cache, significantly accelerating APIs and reducing load on the primary database.

Built extensive CI/CD pipelines (GitLab CI) for automated testing, building, and deployment, making releases much more frequent and stable. Deployed a production EKS cluster on AWS with autoscaling and full fault tolerance. Optimized AWS services configuration (EC2, RDS, S3), reducing costs by 20% without performance degradation. Implemented sharding in MongoDB, enabling the system to handle several-fold growth in user activity. Implemented Redis caching at the API level, cutting response latency by 40–50%. Set up CI/CD with GitHub Actions for backend services, increasing release frequency to 2–3 times per day. Containerized applications with Docker and introduced orchestration via Kubernetes. Set up centralized logging (ELK) and monitoring (Prometheus), reducing mean time to respond to incidents by about one third. Automated infrastructure with Terraform and Ansible, so new environments could be provisioned within hours instead of days. Collaborated daily with analysts and developers to quickly identify and resolve issues.

Supported Linux/Windows server infrastructure and automated routine tasks using Bash and PowerShell scripts. Configured network services (DNS, DHCP, VPN) and ensured stable operation of the office network perimeter. Implemented Zabbix, which helped reduce incident response times by approximately 40%. Organized backup and disaster recovery strategy, eliminating critical downtime caused by data loss. Maintained server security: firewall, updates, and access control. Deployed a Kubernetes cluster for internal company needs and ensured its stable operation. Automated server configuration using Ansible, reducing errors during system rollouts. Optimized load on Linux servers and introduced detailed resource monitoring. Configured PostgreSQL (replication, regular backups, parameter tuning). Used Nginx as a reverse proxy and load balancer, improving stability of internal services.