Role overview
**Senior MLOps Engineer (Hybrid — NYC)**
Pumex Computing is supporting a leading global technology organization in hiring a
**Senior MLOps Engineer**
to drive the next generation of cloud-native, AI-powered platforms.
In this role, you'll design and scale the ML infrastructure that powers intelligent applications used by millions. You’ll work at the intersection of
**ML Engineering, DevOps, and Cloud Platform Architecture**
taking machine learning models from research to secure, scalable real-world deployment.
**What You’ll Do**
* Lead design and automation of
**end-to-end MLOps pipelines**
: model training, deployment, versioning, and monitoring
* Build and operate
**production ML services**
on Kubernetes (GKE)
* Develop
**high-performance Python APIs**
using FastAPI or Flask to serve ML models and platform services
* Architect scalable cloud environments using
**Google Cloud Platform (GCP)**
* Own CI/CD workflows (GitHub Actions) and infrastructure automation (Terraform / Helm / Crossplane)
* Build data ingestion and transformation pipelines supporting ML workloads (Airflow / Dagster)
* Implement advanced
**monitoring & observability**
(Prometheus, Grafana, Cloud Logging)
* Apply security best practices across cloud, data, and service layers
* Collaborate with Data Science & Engineering teams; mentor junior engineers
**What You Bring**
* **5+ years**
in Cloud, DevOps, or Data Engineering roles
* **2+ years hands-on MLOps experience**
in production environments
* Deep Python expertise for API development, automation, and ML pipelines
* Experience deploying
**containerized services**
with Docker & Kubernetes
* Strong knowledge of GCP (BigQuery, Vertex AI, GCS, Pub/Sub preferred)
* CI/CD pipeline experience (GitHub Actions)
* Infra-as-Code (Terraform) & config-as-code (Helm / Crossplane)
* Data pipeline experience (Airflow or Dagster)
* Strong communication and problem-solving abilities
**Compensation & Details**
* Location:
**Hybrid - New York City**
* Full-time, long-term engagement
* Competitive compensation + benefits (details shared during intro call)