Architect, design, and develop secure, high-quality production software systems using Java or Python, integrating AI/ML techniques such as LLMs, Generative AI, and coding assistants
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.
Design experiments, implement algorithms, validate results, and productionize performant, scalable, and trustworthy AI/ML solutions.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Drives decisions that influence the product design, application functionality, and technical operations and processes
Required, qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Auto-GPT)
Advanced proficiency in Java or Python for software system development; strong grasp of software engineering best practices, system design, application development, testing, and operational stability
Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals and coding assistants
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in Java and/or Python programming languages
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.
Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)
Hands-on practical experience delivering system design, application development, testing, and operational stability
Ability to tackle design and functionality problems independently with little to no oversight
Preferred qualifications, capabilities, and skills
Experience working at code level with advanced AI/ML business applications (e.g., LLMs, Generative AI, NLP)
AWS Certifications (Solution Architect Associate or Professional) are advantageous
In-depth knowledge of the financial services industry and their IT systems
