Software Engineer, ML Platform

Zions Bancorporation•Midvale, UT

6h•Hybrid

About The Position

Zions Bancorporation is seeking a Software Engineer, ML Platform to join its Enterprise Technology and Operations team. The role is part of an Agile team focused on productionizing machine learning applications and systems at scale. The engineer contributes to technical design, development, and implementation of machine learning applications using existing and emerging technology platforms, with a focus on foundational ML platform components and the availability and performance of ML services. The role also offers opportunities to apply innovations and best practices in machine learning engineering and model development. The position draws on operations, modeling, data engineering, backend engineering, and ML engineering. It includes building deployment pipelines and containerized services; developing real-time decisioning components; implementing observability; managing the model lifecycle and monitoring; and collaborating across a cross-functional Agile team. The role is hybrid, with a minimum of three days per week in the office at the Zions Technology Center in Midvale, Utah.

Requirements

At least 6 years of professional software engineering experience building and operating production services.
Proficiency in Python, including testing, packaging, and production-quality code.
Experience with Docker and container-based deployment, and working knowledge of Kubernetes.
Experience building or maintaining CI/CD pipelines.
Familiarity with cloud platforms such as GCP, including IAM, networking, and storage basics.
Experience instrumenting production services with metrics, structured logging, and alerting, using tools such as Splunk or Grafana.
Understanding of distributed systems fundamentals such as retries, idempotency, and failure handling.

Nice To Haves

Exposure to MLOps tools such as MLflow, feature stores, or model-serving frameworks.
Experience with streaming platforms such as Kafka.
Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
Experience with Databricks, Spark, or Delta Lake.
Familiarity with rules engines or policy frameworks.

Responsibilities

Build and maintain deployment pipelines, environments, and containerized services for production systems.
Develop components of real-time decisioning services, including event processing, integrations, and decision logging.
Implement production-service observability, including metrics, logging, tracing, alerting, and dashboards.
Design and maintain the model lifecycle, including training pipelines, experiment tracking, model registry, versioning, and promotion from staging to production.
Implement model monitoring for prediction drift, feature drift, data quality, and performance against labeled outcomes.
Collaborate within a cross-functional Agile team to create clean interfaces between data, decision logic, and services.
Use CI/CD best practices to support successful deployment of ML models and application code.
Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices.