Senior MLOps Engineer

Jobtailor; Menomonee Falls (WI); On-site; USD 120,000 - 190,000; Full time

Job summary

Jobtailor in Wisconsin is seeking an experienced ML Engineer to design, deploy, and operate ML solutions, build scalable ETL pipelines, and deploy models into customer-facing apps.

You will enable efficient model development through cloud tooling and maintain ML infrastructure for real-time and batch serving, while collaborating with data scientists and staying current with GCP and MLOps best practices.

Qualifications

• Bachelor’s degree in Data Science or related field.
• 3+ years of experience delivering ML models to production.
• Experience with GCP Vertex AI, BigQuery, and Dataproc.

Responsibilities

• Support cross-functional teams in designing, deploying, and operating machine learning solutions.
• Build and scale ETL pipelines.
• Deploy models into customer-facing applications.
• Enable efficient model development through cloud infrastructure and tooling.
• Design, build, and maintain scalable ML infrastructure for real-time and batch serving, training environments, and orchestration systems.
• Contribute to the ML Engineering and Data Science tools roadmap.
• Develop reusable frameworks and standardized solutions for model implementation.
• Support Data Scientists in using cloud-based tools and infrastructure.
• Collaborate with ML engineers to share knowledge and best practices.
• Develop and maintain monitoring, alerting, and automated testing frameworks.
• Develop, document, and communicate implementations and best practices across the data science lifecycle.
• Manage and communicate cloud infrastructure costs and budgets to stakeholders.
• Stay current with GCP services and MLOps best practices.
• Perform additional assigned tasks.

Skills

MLOps Practices

Google Cloud Platform

Machine Learning Model Deployment

CI/CD Pipelines

Python Programming

Education

Bachelor’s Degree in Data Science

Master’s Degree (Preferred)

Tools

Docker

Kubernetes

Git

TensorFlow

PyTorch

Scikit-learn

Spark

Kubeflow

Airflow

BigQuery

Job description

• Support cross-functional teams in designing, deploying, and operating machine learning solutions
• Build and scale ETL pipelines
• Deploy models into customer-facing applications
• Enable efficient model development through cloud infrastructure and tooling
• Design, build, and maintain scalable ML infrastructure for real-time and batch model serving, training environments, and orchestration systems
• Contribute to the Machine Learning Engineering and Data Science tools roadmap
• Develop reusable frameworks and standardized solutions for model implementation
• Support Data Scientists in using cloud-based tools and infrastructure
• Collaborate with machine learning engineers to share knowledge and improve best practices
• Develop and maintain monitoring, alerting, and automated testing frameworks
• Develop, document, and communicate implementations and best practices across the data science lifecycle
• Manage and communicate cloud infrastructure costs and budgets to project stakeholders
• Stay current with GCP services and MLOps best practices
• Perform additional assigned tasks

Requirements

• Experience in MLOps or DevOps practices, including Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks
• Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
• Experience deploying, scaling, and operationalizing machine learning models in production environments with Data Scientists
• 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
• In-depth knowledge of cloud platforms, preferably Google Cloud Platform, particularly Vertex AI, BigQuery, and Dataproc
• Extensive expertise with CI/CD and IaC best practices
• Extensive knowledge of distributed computing and big data technologies including Spark, Kubeflow, Airflow, and SQL
• Extensive expertise in Python and machine learning libraries such as TensorFlow, PyTorch, and scikit-learn
• Experience working in Agile environments with iterative development and continuous delivery
• Preferred: Master’s Degree
• Preferred: Proficiency in Java or other languages
• Preferred: Retail experience
• Preferred: E-commerce experience
• Preferred: 5+ years of experience in Machine Learning
• Preferred: Experience with optimization techniques and tools such as Gurobi, linear programming, and mixed-integer programming
• Preferred: Experience with agent-based or agentic AI systems, including autonomous workflow or LLM-driven agent orchestration

Core Competencies

Demonstrates expertise in deploying and operationalizing machine learning models using cloud infrastructure, particularly Google Cloud Platform, while supporting cross-functional teams in developing scalable ETL pipelines and MLOps practices. Proficient in building monitoring frameworks and collaborating with Data Scientists to enhance model implementation and best practices.

Highest-signal resume keywords

• MLOps Practices
• Google Cloud Platform
• Machine Learning Model Deployment
• CI/CD Pipelines
• Python Programming

ATS Optimization Keywords

Hard Skills

• Machine Learning Engineering
• ETL Pipeline Development
• Model Serving
• Automated Testing Frameworks
• Distributed Computing
• Big Data Technologies
• Cloud Infrastructure Management
• Data Science Lifecycle
• Optimization Techniques
• Agile Development

Soft Skills

• Collaboration
• Communication
• Knowledge Sharing

Certifications & Qualifications

• Bachelor’s Degree in Data Science
• Master’s Degree (Preferred)

Industry Keywords

• Retail Experience
• E-commerce Experience
• Agent-Based AI Systems

Tools & Technologies

• Docker
• Kubernetes
• Git
• TensorFlow
• PyTorch
• Scikit-learn
• Spark
• Kubeflow
• Airflow
• BigQuery