•Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine approaches to build solutions or break down technical problems
•Develop secure, high-quality production code, and review and debug code written by others
Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability of software applications and systems
•Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability for use within existing systems and information architecture
•Lead communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
•Set and scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establish measurable expectations and ensure consistent validation, security, resiliency, and reuse of proven patterns
•Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets
Required qualifications, capabilities and skills
•Formal training or certification on software engineering concepts and 10+ years applied experience
•Hands-on practical experience delivering system design, application development, testing, and operational stability
•Advanced in Java, React JS, and Spring Boot Framework
•Experience in AWS ECS, EKS, and Terraform
•Demonstrated proficiency in development using agentic development tools, Copilot, and Claude Code
•Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning)
•Proficiency in automation and continuous delivery methods
•Proficient in all aspects of the Software Development Life Cycle
•Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
•Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working, measuring outcomes, and ensuring secure handling of sensitive inputs/outputs
•Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns
