MLOps Engineer

Role Overview

Appiness Inc. is seeking an MLOps Engineer (Google Cloud Platform specialization) to design, implement, and manage scalable machine learning infrastructure on Google Cloud Platform. The role bridges Data Science, Data Engineering, and DevOps to enable end-to-end ML model development, deployment, and monitoring in production.

Responsibilities

• Build and manage scalable end-to-end MLOps pipelines on Google Cloud
• Enable smooth collaboration across Data Science, Data Engineering, and DevOps teams
• Deploy and operate ML workloads in production with monitoring and operational controls
• Implement data/ML infrastructure components supporting ETL/ELT and ML workflows
• Use Infrastructure as Code to provision and manage cloud resources
• Support CI/CD for ML infrastructure and pipelines

Required Qualifications

• 5+ years of experience in MLOps / Machine Learning Engineering
• Strong Python programming skills
• Hands-on experience with Google Cloud Platform services, including:
  • Vertex AI, GKE, Cloud Run, BigQuery, Cloud Storage, Cloud Composer
• Experience with Docker and Kubernetes
• Experience with CI/CD pipelines using GitLab and/or Bitbucket
• Experience with Terraform (IaC)
• Knowledge of ML frameworks: TensorFlow, PyTorch, scikit-learn
• Strong understanding of data pipelines and GCP data services:
  • BigQuery, Dataflow, Dataproc (PySpark)

Preferred Qualifications

• Experience with Vertex AI Pipelines and/or Kubeflow on GKE
• Feature Store implementation
• Exposure to Generative AI and RAG architectures
• Experience with model monitoring and drift detection
• Knowledge of microservices/API development:
  • Cloud Functions, Cloud Endpoints
• Google Cloud certifications:
  • Professional Machine Learning Engineer
  • Professional Cloud Architect

Location / Work Mode

• Denver, CO (Remote)