SENIOR MLOPS ENGINEER (Google Cloud Platform) — ML

Location: 2 days per week in Austin, TX – central/downtown area

Contract to hire – candidates who do not require sponsorship

Skills Needed:

1. Platform Architecture
2. Google Cloud Platform & Vertex AI
3. Infrastructure as code
4. CI/CD/CT Pipelines
5. Observability & Drift
6. Kubeflow or Databricks

Senior MLOps Engineer (Google Cloud Platform) — ML

Platform Framework
We are seeking a Senior MLOps Engineer to architect and build our next-generation, end-to-
end ML Platform-as-a-Product. In this role, you will design the unified framework that
abstracts infrastructure complexities, enabling ML Engineers and Data Scientists to seamlessly
write, train, deploy, and monitor models in production.

Key Responsibilities
ML Lifecycle Stage Core Focus & Deliverables
Authoring & Local
Dev
Standardize notebook environments, local CLI tools, and SDKs to make
model writing highly repeatable and containerized from day one.
Training &
Orchestration
Build automated, scalable training pipelines utilizing Vertex AI
Pipelines, Kubeflow, and Databricks, integrating unified data access
layers.
Productionalization
Construct robust CI/CD/CT (Continuous Training) pipelines that
packaging models into auto-scaling microservices on Google
Kubernetes Engine (GKE) or Vertex AI Endpoints.
Operations & Drift
Implement end-to-end observability frameworks to track system
performance, monitor data/concept drift, and trigger automated alert-
and-retrain loops.
Platform Governance Manage the enterprise Feature Store, artifact registries, and unified
metadata tracking (lineage) for compliance and governance.

Technical Requirements & Stack
• Platform Architecture: Proven experience building a unified "ML Platform-as-a-
Service" inner-source framework for internal engineering consumption.
• Google Cloud Platform Ecosystem: Deep expertise in Vertex AI (Pipelines, Feature Store, Model
Registry, Endpoints), GKE, BigQuery, and Google Cloud Storage.
• Infrastructure as Code (IaC): Strong hands-on experience using Terraform to
provision secure, compliant, and cost-optimized ML infrastructure.
• Maturity Frameworks: Experience setting up end-to-end automation covering ML
GitOps, automated testing, and zero-downtime canary deployments.

Key Takeaway
Your objective is simple: Reduce time-to-market for ML models from months to minutes by
delivering a friction-free, standardized, and self-service engineering framework