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ML/ML Ops Principal Engineer

LinkedIn HD Supply Atlanta, GA
Not Applicable Posted April 1, 2026 Job link
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Requirements
  • 7-10+ years of experience in Data Science, Machine Learning, and ML Operations
  • Cloud Architecture certification like (Google Cloud Professional Architect, etc.)
  • Certification in Artificial Intelligence like (Google Cloud, Vertex AI, etc)
  • Build Machine Learning models, perform proof-of-concept, experiments, optimize, and deploy models into production
  • Expertise in designing ML models using TensorFlow or PyTorch.
  • Demonstrated experience deploying ML models in production environments
  • Strong understanding of feature engineering, model evaluation, and bias testing.
  • Theoretical and Practical knowledge in developing regression, classification, and time-series models.
  • Proficient in MLOps and DataOps using MLflow, Kubeflow, and CI/CD
  • Expertise in model monitoring, retraining strategies, deployment strategies, and cost optimization.
  • Deep knowledge of cloud-native architecture and AI/ML services on GCP, AWS or Azure.
  • Skilled in building scalable APIs, microservices, and event-driven systems
  • Provide technical leadership to team of strong engineers
  • Experience with Python, SQL, and model-serving frameworks
  • Expertise in applying ML to topics in pricing, product recommendation, supply chain, and shared services
  • Experience working with Vertex AI for scalable ML pipeline deployment
  • Experience with semantic search, vector databases, and graph technologies
  • Strong understanding of data governance, security, and compliance in distributed systems
  • Familiarity with enterprise data platforms
  • Generally 7+ years of experience in a related field.
Preferred Skills
  • 7-10+ years of experience in Data Science, Machine Learning, and ML Operations
  • Cloud Architecture certification like (Google Cloud Professional Architect, etc.)
  • GCP is highly preferred.
  • Experience with Python, SQL, and model-serving frameworks
  • Experience working with Vertex AI for scalable ML pipeline deployment
  • Experience with semantic search, vector databases, and graph technologies
  • Familiarity with enterprise data platforms
  • Advanced degree may offset less experience in some disciplines.
Education
  • (Not required) – Bachelor’s or Master’s degree in computer science, engineer, mathematics, data science or a related technical field
  • (Not required) – Certification in Artificial Intelligence like (Google Cloud, Vertex AI, etc)
  • (Not required) – Typically requires BS/BA in a related discipline.
  • (Not required) – Generally 7+ years of experience in a related field.
  • (Not required) – Advanced degree may offset less experience in some disciplines.