Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Drive communities of practice across Software Engineering to promote new and leading-edge technologies
Foster a team culture of diversity, opportunity, inclusion, and respect
Required Qualifications, Capabilities, and Skills
Proficiency in Engineering & Architecture, AI/ML, with hands-on experience designing, implementing, testing, and ensuring operational stability of large-scale enterprise data platforms
Advanced skills in one or more programming languages such as Java, Python, C/C++, or C#
Practical experience delivering system design, application development, testing, and operational stability
Working knowledge of relational and NoSQL databases and data lake architectures
Experience developing, debugging, and maintaining code with modern programming languages and database querying languages
Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, Observability tools (Dynatrace, Splunk, Grafana), and Orchestration tools (Airflow, Temporal)
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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; experience coaching senior engineers/leads on compliant usage patterns and controls.
Proficiency in automation, continuous delivery methods, and all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies, CI/CD, application resiliency, and security
Practical cloud-native experience
Preferred Qualifications, Capabilities, and Skills
Experience with modern data technologies such as Databricks or Snowflake
Hands-on experience with Spark/PySpark and other big data processing technologies
Demonstrated proficiency in software applications and technical processes within disciplines such as data engineering, cloud, artificial intelligence, machine learning, or mobile
Knowledge of the financial services industry and their IT systems
