Senior Manager of Software Engineering - ML / Agentic Treasury

• Provide overall direction, oversight, and coaching for a team of entry-level to mid-level software engineers that work on basic to moderately complex tasks

• Accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures

• Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures 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.

• Ensures successful collaboration across teams and stakeholders

• Identifies and mitigates issues to execute a book of work while escalating issues as necessary

• Provides input to leadership regarding budget, approach, and technical considerations to improve operational efficiencies and functionality for the team

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise

• Hands-on practical experience in system design, application development, testing, and operational stability

• Experience in developing, debugging, and maintaining code in a large corporate environment with Java and Python programming languages and database querying languages (SQL)
• Experience with PostgreSQL
• Experience with AWS
• 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.
• Overall knowledge of the Software Development Life Cycle
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

Preferred qualifications, capabilities, and skills

• Preferred knowledge with Databricks
• Preferred knowledge with ML Frameworks including Scikit-Learn, PyTorch, Pandas