DevOps Engineer

• Design and implement end-to-end MLOps pipelines for training, deployment, and inference.
• Build and maintain Python-based APIs (FastAPI) to support ML workflows and data ingestion.
• Configure and manage cloud infrastructure (AWS, GCP, Azure) to support AI/ML workloads.
• Automate deployment, scaling, and monitoring of models using Docker and CI/CD pipelines.
• Implement observability, logging, and alerting with tools like CloudWatch, Prometheus, Grafana, and GCP logging.
• Collaborate with cross-functional teams to define requirements and deliver scalable MLOps solutions.
• Optimize model performance and system reliability while troubleshooting production issues.
• Apply infrastructure-as-code practices (Terraform) for reproducible and maintainable deployments.
• Stay ahead of industry best practices in MLOps, cloud-native engineering, and AI/ML integration.

Senior ML and DevOps Engineer – Advertising

GCP AI Platform MLOps Engineer – DevOps, Machine Learning Operations

Senior DevOps Engineer – AWS, Contract

DevOps Engineer, Bilingue

Site Reliability Engineer, AWS

Senior DevOps Engineer