Apply 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.
Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Lead 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.
Required Qualifications, Capabilities, and Skills
Formal training or certification on software engineering concepts and advanced applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Advanced in one or more programming languages, preferrably Java.
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.
Proficient in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
In-depth knowledge of the financial services industry and their IT systems.
Practical cloud native experience.
Preferred Qualifications, Capabilities, and Skills
Experience working in big data environments with structured, semi-structured, and unstructured data.
Hands-on practical experience in system design, application development, testing, and operational stability.
Advanced knowledge of Java programming language.
In-depth knowledge of software applications and technical processes in areas such as cloud, AI, machine learning, or mobile.
Ability to tackle design and functionality problems independently with minimal oversight.
Proficiency in application, data, and infrastructure architecture disciplines.
Proficiency in cloud-native microservices architecture, design, and implementation.
Proficiency in event-driven architecture and application containerization.
Experience building applications on public cloud platforms like AWS, GCP, and Azure, with a preference for AWS.
Proficiency in real-time streaming applications using Apache Spark Streaming, Apache Kafka, Amazon Kinesis, and emerging cloud serverless managed services.
