Senior MLOps Engineer – Cloud Inference & Security

Meridial; Buenos Aires; Vor Ort; ARS 139.086.000 - 216.357.000; Vollzeit

Benefits dieser Stelle

Competitive compensation

Comprehensive benefits

Flexible work environment

Wellness days

Global collaboration

Zusammenfassung

Proofpoint is seeking a Senior MLOps Engineer to design and operate the infrastructure that brings machine learning models from development to production. You will influence how fast and safely models are shipped while aligning with security and governance standards.

You will collaborate with data scientists and ML engineers to streamline experiments to production, maintain CI/CD pipelines, and ensure scalable, secure inference endpoints across cloud environments.

Qualifikationen

• 5+ years of ML experience with PyTorch or TensorFlow.
• Proficiency in Python and infra-as-code tools (Terraform, Helm).
• Strong Kubernetes production experience.
• Hands-on CI/CD (Harness, Jenkins, GitHub Actions).
• Familiarity with container security and vulnerability management.
• Experience with AWS (SageMaker, EKS) and Azure (AKS).
• Knowledge of FedRAMP/SOC2 or similar security frameworks.
• ML model serving experience (TorchServe, Triton).
• Strong SQL for telemetry data.

Aufgaben

• Design, build, and maintain ML CI/CD pipelines.
• Manage container images and base image security.
• Deploy ML inference endpoints on Kubernetes across AWS/Azure.
• Lead FedRAMP compliance efforts for ML infra.
• Manage GPU nodes and compute clusters for model serving.
• Collaborate with data scientists to move experiments to production.
• Monitor infrastructure health, performance, and costs.
• Document deployment processes and security reviews.
• Stay current with MLOps tooling and cloud-native security.

Kenntnisse

Python

Kubernetes

CI/CD

PyTorch

TensorFlow

Terraform

Helm

GitHub Actions

Jenkins

Snyk

AWS

Azure

SQL

Prometheus

Grafana

Tools

Harness

Terraform

Helm

Kubernetes

Jenkins

Jobbeschreibung

Proofpoint is seeking a Senior MLOps Engineer to design and operate the infrastructure that brings machine learning models from development to production. You will influence how fast and safely models are shipped while aligning with security and governance standards.

You will collaborate with data scientists and ML engineers to streamline experiments to production, maintain CI/CD pipelines, and ensure scalable, secure inference endpoints across cloud environments.