Machine Learning Engineer II (Remote)

📋 Description

• • Architect and deploy end-to-end machine-learning pipelines that power Kohl’s digital experiences for millions of shoppers. You will translate Data-Science prototypes into resilient, low-latency services running on Google Cloud Platform, ensuring every recommendation, search ranking, and personalization model meets production-grade reliability.
• • Partner daily with Product, Engineering, and Data-Science squads to integrate new models into customer-facing web, mobile, and omnichannel touchpoints. You will own the technical design for model serving, A/B test instrumentation, and rollback strategies, turning experimental notebooks into revenue-generating features.
• • Evolve our internal MLE platform by building reusable toolsets—SDKs, templates, CI/CD blueprints, and Terraform modules—that cut model deployment time from weeks to hours. Your contributions will be adopted across dozens of teams, establishing Kohl’s as a benchmark for scalable retail ML engineering.
• • Design and implement comprehensive monitoring, alerting, and observability layers that track data drift, model accuracy, latency, and cost in real time. When thresholds breach, you will lead incident response, root-cause analysis, and automated remediation to protect customer trust and business KPIs.
• • Optimize distributed ETL and training workflows using Spark on Dataproc, Airflow orchestration, and BigQuery analytics. You will profile pipelines, tune resource allocation, and introduce caching or incremental strategies to reduce runtimes and cloud spend while handling terabyte-scale retail data.
• • Champion MLOps best practices: containerization with Docker, CI/CD via Cloud Build, infrastructure-as-code with Terraform, feature stores, model registries, and reproducible experiment tracking. You will write clean, tested Python and peer-review code to uphold engineering excellence.
• • Mentor junior engineers through pair programming, design reviews, and brown-bag sessions. You will craft learning paths, define coding standards, and foster a culture where curiosity and knowledge-sharing accelerate team velocity.
• • Contribute to the strategic roadmap for Data-Science and MLE technology, evaluating new GCP services (Vertex AI Pipelines, BigQuery ML, TensorFlow Serving) and open-source tools. Your recommendations will shape multi-year investments and keep Kohl’s at the forefront of retail AI innovation.
• • Iterate in two-week Agile sprints using Extreme Programming (XP) practices—TDD, continuous integration, and collective code ownership. You will demo progress to stakeholders, incorporate feedback rapidly, and document decisions for long-term maintainability.
• • Stay ahead of industry trends in machine-learning engineering, distributed systems, and cloud cost optimization. You will experiment with emerging frameworks, share findings in guild meetings, and translate research into practical enhancements for our production stack.