GCP AI & Data Platform Engineer | MLOps & LLM Workflows

SNI; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

SNI is seeking an experienced ML Engineer to design, develop, and deploy enterprise data platforms and AI workflows on Google Cloud. You will architect end-to-end cloud solutions, guide customer projects to completion, and build scalable architectures with AlloyDB, GCS, and GKE.

The role requires 5+ years of hands-on experience, and a Google Cloud Professional ML Engineer certification is mandatory. Expect collaboration with Data Scientists and Software Engineers to deliver production-grade

Qualifications

• Certification: Google Cloud Professional ML Engineer (mandatory).
• Experience: 5+ years designing, building, and deploying high-performance data platforms and AI/agentic solutions.
• Hands-on with AlloyDB, GCS, GKE, Gemini Enterprise, and Vertex AI.
• CI/CD and MLOps practices for production AI/ML pipelines.

Responsibilities

• Design, develop, and deploy enterprise data platforms and agentic solutions on Google Cloud Platform.
• Architect end-to-end cloud enterprise solutions and guide customer projects through technical completion.
• Build scalable database, storage, and containerized architectures using AlloyDB, GCS, and GKE.
• Implement advanced LLM/AI workflows using Gemini Enterprise and Agent Platform (formerly Vertex AI).
• Automate infrastructure provisioning, CI/CD pipelines, and MLOps practices for seamless model deployment and monitoring.
• Write high-performance, maintainable Python code adhering to modern software design patterns, algorithms, and data structures.

Skills

Python

Cloud architecture

MLOps

GCP

Data platforms

AI workflows

Tools

AlloyDB

GCS

GKE

Gemini Enterprise

Vertex AI

Job description

SNI is seeking an experienced ML Engineer to design, develop, and deploy enterprise data platforms and AI workflows on Google Cloud. You will architect end-to-end cloud solutions, guide customer projects to completion, and build scalable architectures with AlloyDB, GCS, and GKE.

The role requires 5+ years of hands-on experience, and a Google Cloud Professional ML Engineer certification is mandatory. Expect collaboration with Data Scientists and Software Engineers to deliver production-grade