AI Engineer

Jobtailor; Troy (MO); On-site; USD 120,000 - 160,000; Full time

Job summary

Jobtailor in the United States is seeking an experienced machine learning engineer to design, build, and deploy scalable ML solutions using Gemini Enterprise and Google Cloud services.

You will develop and maintain MLOps pipelines, provision cloud infrastructure with Terraform, and deploy ML workloads on Google Kubernetes Engine (GKE) with secure VPC networking, collaborating with data scientists and engineers to deliver production-ready AI solutions.

Qualifications

• Experience designing, building, and deploying scalable ML solutions on Gemini Enterprise and Google Cloud.
• Experience developing and maintaining MLOps pipelines for training, deployment, monitoring, and lifecycle management.
• Experience provisioning and managing cloud infrastructure using Terraform.
• Experience deploying ML workloads on GKE with secure VPC networking.
• Strong collaboration with data scientists and engineers to deliver production-ready AI solutions.

Responsibilities

• Design, build, and deploy scalable ML solutions using Gemini Enterprise and Google Cloud.
• Develop and maintain MLOps pipelines for training, deployment, monitoring, and lifecycle management.
• Provision and manage cloud infrastructure with Terraform ensuring automation and compliance.
• Deploy ML workloads on GKE with secure and optimized VPC networking.
• Collaborate with data scientists, engineers, and stakeholders to deliver production-ready AI solutions.

Skills

Machine Learning Engineering Expertise

Google Cloud Platform (GCP) Expertise

MLOps Pipeline Development

Terraform Infrastructure Management

Google Kubernetes Engine (GKE) Deploym

Tools

Gemini Enterprise

Google Cloud Services

Terraform

Google Kubernetes Engine (GKE)

Job description

• Design, build, and deploy scalable machine learning solutions using Gemini Enterprise and Google Cloud services
• Develop and maintain MLOps pipelines for model training, deployment, monitoring, and lifecycle management
• Provision and manage cloud infrastructure using Terraform, ensuring automation and compliance
• Deploy and operate ML workloads on Google Kubernetes Engine (GKE) with secure and optimized VPC networking
• Collaborate with data scientists, engineers, and business stakeholders to deliver production-ready AI solutions

Requirements

• Experience designing, building, and deploying scalable machine learning solutions using Gemini Enterprise and Google Cloud services
• Experience developing and maintaining MLOps pipelines for model training, deployment, monitoring, and lifecycle management
• Experience provisioning and managing cloud infrastructure using Terraform
• Experience deploying and operating ML workloads on Google Kubernetes Engine (GKE)
• Knowledge of secure and optimized Virtual Private Cloud (VPC) networking
• Cloud architecture and deployment expertise
• Machine learning engineering expertise
• Google Cloud Platform (GCP) expertise

Core Competencies

Demonstrates expertise in designing and deploying scalable machine learning solutions using Gemini Enterprise and Google Cloud services, alongside proficiency in MLOps pipelines and cloud infrastructure management with Terraform and Google Kubernetes Engine (GKE). Strong collaboration skills with data scientists and business stakeholders to deliver production-ready AI solutions.

Highest-signal resume keywords

• Machine Learning Engineering Expertise
• Google Cloud Platform (GCP) Expertise
• MLOps Pipeline Development
• Terraform Infrastructure Management
• Google Kubernetes Engine (GKE) Deployment

Hard Skills

• Machine Learning Solutions Design
• MLOps Pipeline Maintenance
• Cloud Infrastructure Provisioning
• Google Cloud Services
• Terraform
• Google Kubernetes Engine (GKE)
• Virtual Private Cloud (VPC) Networking
• Cloud Architecture
• Model Training
• Model Deployment

Soft Skills

• Collaboration
• Communication