Senior MLOps Engineer (Google Cloud Platform) In Austin, TX || Hybrid Role || Locals only

Innovise; Austin (TX); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Innovise in Austin, TX is seeking a Senior MLOps Engineer to architect and build a next-generation ML Platform-as-a-Product. You will design a unified framework that abstracts infrastructure complexity, enabling ML Engineers and Data Scientists to write, train, deploy, and monitor models in production.

The role is hybrid in Austin, requiring 2 days on-site per week, with a 12+ month duration. Candidates should have cloud and MLOps expertise to drive platform reliability and scalability.

Qualifications

• Experience shaping ML platforms and end-to-end ML pipelines.
• Strong expertise in cloud platforms and ML tooling.
• Knowledge of IaC, CI/CD and observability practices.
• Familiarity with MLOps patterns and model monitoring.

Responsibilities

• Architect and build a next-gen ML Platform-as-a-Product.
• Design a unified framework to abstract infra complexity for ML teams.
• Enable data scientists to write, train, deploy and monitor models in production.

Skills

Platform architecture

Google Cloud Platform

Vertex AI

Infrastructure as code

CI/CD pipelines

Observability

Drift management

Kubeflow

Databricks

Tools

Google Cloud Platform

Vertex AI

Kubeflow

Databricks

Job description

Senior MLOps Engineer (Google Cloud Platform) ML

Duration: 12+ Months

Austin, TX central/downtown area || Hybrid Role

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

Role: Senior MLOps Engineer (Google Cloud Platform) ML

Duration: 12+ Months

Location: Austin, TX central/downtown area || Hybrid Role

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

Job Description-

Skills Needed

• Platform Architecture
• Google Cloud Platform & Vertex AI
• Infrastructure as code
• CI/CD/CT Pipelines
• Observability & Drift
• Kubeflow or Databricks

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.