Staff Software Engineer

Acts as a project or system leader, coordinating the activities of other engineers on the project or within the system Determines the technical tasks that other engineers will follow Actions result in the achievement of customer, operational, program or service objectives Proactively improves existing structures & processes Exercises judgement in reconciling diverse and competing priorities (time, quality, complexity risk) to identify optimal solutions to problems Notices patterns and condenses repetition into densely meaningful generalized solutions Collaborates with management to set/improve standards for engineering rigor - Ensure the stability and reliability of the platform on a daily basis to meet business requirements and SLAs. - Develop new platform features and capabilities to support emerging use cases based on requirements from product teams and data scientists. - Respond promptly to platform issues as L3 support, providing expert troubleshooting and resolution for complex technical problems. - Collaborate with cross-functional teams on key project delivery, including ML model deployment infrastructure and platform optimization initiatives. - Mentor and guide other engineers in best practices for ML platform development and operations. 5+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. - Excellent problem-solving skills and clear communication abilities to work effectively in an agile/scrum team environment. - Strong proficiency in Java development with focus on backend technologies and hands-on experience with Spring framework. - Familiarity with Big Data technologies such as Hadoop, Spark, HBase, Iceberg, or DeltaLake. - Experience with containerization and orchestration using Docker and Kubernetes. - Working knowledge of GCP stack, including Bigtable, BigQuery, and GKE (Google Kubernetes Engine). - Solid understanding of Linux systems and Bash scripting for automation and platform operations. - Experience building or supporting ML/AI platforms or data pipelines. - Knowledge of ML model lifecycle management and MLOps practices.