Identify opportunities to automate and remediate recurring issues, improving overall system reliability.
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.
Applies 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.
Required qualifications, capabilities, and skills
Formal training or certification in 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
Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade solutions
Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)
Proficiency in automation, continuous delivery (CI/CD), and cloud-native development (preferably AWS)
Hands-on practical experience delivering system design, application development, testing, and operational stability
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
Practical cloud native experience
