Juan R.

Azure Architecture & Deployment:

• Designed and implemented scalable, resilient cloud architectures on Microsoft Azure.
• Led infrastructure deployments using Terraform, managing over 10 integrated environments.
• Built hybrid and multi-region solutions, with a focus on compliance, security and cost efficiency.

DevOps & Automation:

• Administered full Azure DevOps stack (repos, pipelines, artifacts) across all projects.
• Developed complete CI/CD workflows from scratch, enabling fast and safe deployments.
• Migrated legacy processes to GitOps-based workflows, increasing traceability and reducing manual errors.

Kubernetes Cluster Management:

• Took ownership of all Kubernetes (AKS) clusters across environments.
• Improved cluster reliability, observability and security.
• Built internal tools and scripts to simplify cluster operations, deployments, scaling and issue resolution.

Cost Optimization & Observability:

• Reduced Azure cloud spend by 25% through smart autoscaling, cost-aware provisioning and off-hours shutdown policies.
• Implemented custom dashboards and alerting systems to anticipate and mitigate critical incidents.

Scripting & Tooling:

• Automated reporting and cloud audits with custom Bash and Python scripts.
• Collaborated with data teams to extract operational metrics from logs and CI pipelines.

Key achievements:

• Designed a cloud architecture template adopted across international teams.
• Served as internal expert and trainer on Terraform, Kubernetes and DevOps best practices.

AI & Optimization for Marketing:

• Designed and deployed cutting-edge marketing optimizers for budget allocation and audience targeting, directly increasing campaign ROI.
• Built predictive demand models and marketing mix optimization tools using Pyomo and advanced numerical methods.
• Reduced inference times by over 40% via deep model refactoring and optimization techniques.

MLOps & Full Pipeline Automation:

• Rebuilt legacy pipelines from scratch using Spark + Airflow, automating the full ML lifecycle from ingestion to monitoring.
• Implemented GitOps workflows using GitLab CI/CD and infrastructure as code (IaC) for reproducible and robust deployments.
• Standardized deployment patterns for model versioning, rollback, and dynamic scaling in production.

Cloud Migration & Scalability:

• Migrated ML workloads and critical systems from legacy infrastructure to Google Cloud Platform and AWS.

Kubernetes & Infrastructure Engineering:

• Deployed all inference workloads on Kubernetes, using Helm and custom operators for streamlined management.

Backend Engineering for ML Services:

• Built and maintained Python microservices for model inference, feature stores, and internal tooling.
• Designed REST APIs enabling real-time integration with marketing platforms and business dashboards.

Key Tech Stack:

• Languages: Python (expert), Bash
• Libraries: Pyomo, Pandas, NumPy, Scikit-learn
• Data & Pipelines: Spark, Airflow
• Cloud: AWS, GCP
• Infrastructure: Kubernetes, Docker, GitLab, Terraform, GitOps
• Specialties: MLOps, DevOps, Optimization, Backend APIs, Scalable ML

Backend Architecture & API Development:

• Designed the complete backend system using AWS Lambda, API Gateway, and DynamoDB — ensuring scalability, low-latency, and cost-efficiency.
• Built event-driven architectures using CloudWatch, SQS, and SNS to handle asynchronous workflows and integrate external services.

DevOps, CI/CD & Infrastructure:

• Developed and maintained CI/CD pipelines using CloudFormation and GitHub Actions for secure, reproducible deployments.
• Defined IaC policies covering security, networking (VPCs, subnets, routing), and IAM roles for least-privilege access control.
• Reduced operational costs by more than 30% through architectural refactoring and intelligent resource provisioning.

Leadership & Cross-Functional Delivery:

• Acted as the technical interface between engineering and business stakeholders, ensuring architectural alignment with product goals.
• Coordinated frontend and backend teams to deliver key features under aggressive timelines.
• Led code reviews, mentoring and definition of best practices in backend engineering.

Web Portal Ownership:

• Maintained and optimized the company’s customer-facing web portal, ensuring high availability and rapid incident resolution.

Key Technologies:

• Languages & Frameworks: Python, FastAPI, REST APIs
• Cloud: AWS Lambda, DynamoDB, S3, API Gateway, IAM, VPC
• IaC & DevOps: CloudFormation, GitHub Actions, CI/CD, Monitoring
• Security: Encryption at rest/in-transit, IAM policies, private networking