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 capabilities, to improve the value realized by automation
Collaborate with cross-functional teams including product, architecture, and business stakeholders to translate requirements into technical solutions
Conduct thorough code reviews and establish engineering best practices that elevate the quality and consistency of the team's output
Mentor and coach junior engineers, fostering a culture of learning, ownership, and technical growth
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and advanced applied experience
Advanced proficiency in Java, including experience building and maintaining large-scale, production-grade applications
Hands-on experience designing and deploying cloud-native solutions on AWS (e.g., EC2, S3, Lambda, RDS, EKS)
Demonstrated experience building and maintaining CI/CD pipelines using industry-standard tools
Strong understanding of software design patterns, distributed systems, and microservices architecture
Proven ability to lead technical discussions, influence design decisions, and drive alignment across engineering teams
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
Experience with infrastructure-as-code tools such as Terraform or AWS CloudFormation
Familiarity with containerization and orchestration technologies such as Docker and Kubernetes
Exposure to observability and monitoring practices using tools such as Datadog, Splunk, or equivalent platforms
Experience contributing to or leading architectural decisions in a large, matrixed enterprise environment
Knowledge of security best practices in cloud and application development contexts
