Sets 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
Applies 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
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
Preferred qualifications, capabilities, and skills
Familiarity with Databricks
Exposure to cloud technologies
Experience with Typescript
Knowledge on API / Microservices
