Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation
Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong Java development expertise, including technologies such as Spring/Spring Boot, Hibernate, JMS, and RESTful API design and implementation.
Experience deploying and monitoring applications on AWS Cloud including services such as S3, Lambdas, Security, and IAM
Hands-on practical experience delivering system design, application development, testing, and operational stability
Strong background in designing complex applications and documenting architecture artifacts (e.g., diagrams, standards, decision records)
Proficiency in automation and continuous delivery methods
Proficient in all aspects of the Software Development Life Cycle
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
Proficiency in databases like Aurora PostgreSQL
Expertise in using monitoring tools like Splunk, Grafana and creating dashboards and alerts
AWS Developer or Architect Certifications
