Senior MLOps Engineer

• 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

• Experience in MLOps or Dev Ops 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, Big Query, 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 Tensor Flow, 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

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.

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

• 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

• Collaboration
• Communication
• Knowledge Sharing

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

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

• Docker
• Kubernetes
• Git
• Tensor Flow
• Py Torch
• Scikit-learn
• Spark
• Kubeflow
• Airflow
• Big Query