Produces or contributes to architecture and design artifacts for applications while ensuring design constraints are met by software code development
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene, data quality, and system architecture
Contributes to software engineering communities of practice and events that explore new and emerging technologies, including modern AI engineering patterns
AI-first / prompt-oriented development responsibilities (embedded into delivery):
Designs and implements LLM-enabled features (e.g., summarization, extraction, Q&A, reasoning assistants) with attention to reliability, latency, cost, and user experience
Practices prompt-oriented development: iterating on prompts and system instructions as first-class artifacts, using versioning, test cases, and measurable evaluation criteria (quality, groundedness, safety)
Implements patterns such as retrieval-augmented generation (RAG), tool/function calling, workflow orchestration, and human-in-the-loop review where appropriate
Builds evaluation and monitoring approaches for AI features (automated checks, regression tests, guardrails, observability) and continuously improves quality based on production feedback
Applies secure engineering practices to AI-enabled systems, including data minimization, access controls, safe handling of sensitive information, and resilience against prompt injection and misuse
Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Hands-on practical experience in system design, application development, testing, and operational stability
Proficient in coding in Java or Python
Experience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Overall knowledge of the Software Development Life Cycle
Understanding of agile methodologies such as CI/CD, application resiliency, and security
Knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Demonstrated ability to deliver AI-enabled capabilities responsibly, including comfort working with prompting techniques, structured inputs/outputs, evaluation approaches, and iterative refinement in partnership with product and users
Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies (e.g., React)
Exposure to cloud technologies (e.g., AWS)
Knowledge and interest in the financial industry
Experience with applied AI engineering practices (any of the following): RAG, semantic search, embeddings, prompt/version management, LLM evaluations, model/service integration patterns, or AI feature monitoring in production
