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
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies and partner with cross-functional stakeholders (engineering, product, operations, risk/security) to refine requirements, propose architectures, and deliver outcomes aligned to customer needs and business goals
Debug and troubleshoot complex issues in distributed systems with leading root-cause analysis and drive long-term fixes
Mentors junior engineers through coaching, pairing, reviews, and knowledge sharing—raising engineering standards across the team.
Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Demonstrated strength in system design, application development, testing strategies, and production operations/stability
Advanced in one or more programming language(s) and framework(s) (e.g., Python, Java, Fast API, Django, Rest API design, Containers, etc.)
Experience with distributed systems and messaging, such as RabbitMQ (or equivalent)
Experience with modern data stores such as Cockroach DB (or equivalent distributed SQL/NoSQL systems).
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 with experience coaching engineers on safe, compliant and adoption within delivery practices
Proficient in all aspects of the Software Development Life Cycle from requitements and design through production support and continuous enhancement
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, including automation (testing, deployment practices, secure coding, least privilege, incident response, SLOs/SLIs)
Practical cloud-native experience (e.g., containerization, infrastructure-as-code concepts, managed services), including exposure to AWS
Preferred qualifications, capabilities, and skills
Experience with storage systems, infrastructure technologies, or platform engineering domains
Exposure to modern and emerging technologies (e.g., event-driven architectures, service mesh, zero trust patterns, AI-assisted engineering)
Experience modernizing legacy systems and improving reliability at scale
