Agustín Aguilera Conti

Cloud Engineer – AI Orchestrator Team

Avature S.R.L. | Mar 2026 – Present | Remote

• Own and operate the cloud infrastructure supporting AI-driven backend services in production, applying DevOps and SRE practices to maintain availability, resilience, and reliability.
• Build and automate development and production environments using infrastructure as code and configuration management to ensure consistent, reproducible setups.
• Manage Kubernetes infrastructure and service deployments, supporting stable rollouts and troubleshooting deployment issues across environments.
• Build and maintain CI/CD pipelines for container images and service deployments, improving delivery consistency and reducing manual intervention.
• Implement and maintain metrics, dashboards, and alerting to monitor service health, detect operational issues, and support incident diagnosis.
• Analyze cloud costs and resource utilization to identify optimization opportunities and support capacity planning, balancing performance, reliability, and cost efficiency.
• Maintain Linux-based infrastructure, resolve production incidents, and participate in on-call rotations.
• Collaborate with engineers across disciplines throughout the full service lifecycle, from development and deployment to ongoing production operations.

Cloud Engineer – Cloud Operations Team

Avature S.R.L. | Mar 2024 – Mar 2026 | Remote

• Managed and monitored production infrastructure in AWS and Linux environments to ensure availability and performance.
• Automated operational processes using Terraform, Puppet, and Bash/Python scripting, significantly reducing critical task execution times.
• Addressed and resolved critical incidents during on-call shifts, responding to alerts generated in Splunk.
• Coordinated operational tasks and led technical onboarding processes in English for new team members.
• Authored and maintained documentation to optimize incident resolution and address large-scale data location issues in S3.
• Recognized as a technical referent within the Cloud Operations team, supporting peers and other teams in planning, troubleshooting, and decision-making for high-impact cases.
• Led long-running and highly complex operational initiatives (e.g., S3 data location, bucket housekeeping, node reboot strategies, alerting improvements), ensuring continuity, documentation, and stakeholder alignment.
• Designed and implemented an alert analysis and optimization framework that reduced unnecessary noise during on-call rotations, improving team efficiency and quality of life.
• Demonstrated full autonomy in organizing and executing critical tasks while proactively communicating progress to leadership and stakeholders.
• Drove cross-team collaboration, regularly assisting other areas and participating in weekend labor to unblock processes and accelerate delivery.
• Delivered consistently high-quality solutions with an “extra mile” approach, including automation scripts, internal tooling, and improved procedures adopted by the wider team.

Infrastructure Implementation for Machine Learning (Internship)

Universidad Nacional de Río Cuarto | Sep 2023 – Dec 2023 | Remote

• Deployed a Kubernetes cluster and Kubeflow platform to centralize and optimize compute resources for data science projects.
• Developed reusable configurations with Ansible, ensuring scalability and reproducibility.
• Resolved compatibility and dependency issues in Linux environments to guarantee stable and production-ready setups.

Bachelor’s Degree in Telecommunications Engineering

Universidad Nacional de Río Cuarto | 2017 – 2023 | GPA: 8.07/10 (US GPA: 3.64)

High School Diploma (Business Administration & Non-profit Organizations)

Colegio Hispano Argentino | 2011 – 2016 | GPA: 8.86

Testing Apps TCP/IP

github.com/aagustinconti/testing_apps_tcpip

Final project for the TCP/IP Applications course in Telecommunications Engineering. Deployed a hardware store inventory application (developed by a teammate) on Kubernetes and implemented Grafana on K8s for monitoring. Performed performance testing with K6, measured metrics, and analyzed automatic pod scaling to assess system resilience and efficiency.

YOLOv7 Counting

github.com/aagustinconti/yolov7_counting

Implementation of an object detection, tracking, and counting system based on YOLOv7. Configured and fine-tuned the model to identify different object classes in images and videos. Adapted the inference pipeline to log and visualize counts in real time, optimizing parameters to improve accuracy in specific scenarios. Focused on applications of automatic counting in video analytics contexts.