Original listing text, shown exactly as published by the company.
Expected Duties
AI Feature Architecture & Technical Direction
- Own the reference architecture for customer-facing AI features, including LLM integration patterns, prompt management, context strategies, retrieval design, and response validation
- Lead architecture reviews for new AI features, setting the technical standard for how AI capabilities are designed and evaluated before implementation begins
- Drive build-vs-integrate decisions for AI feature components, evaluating third-party tooling, platform capabilities, and custom development tradeoffs
- Define and document API contracts, data flows, and system integration patterns for AI features that span product surfaces
AI Product Delivery
- Contribute directly to AI feature implementation across the full stack: backend LLM integrations in Python, RESTful service design, and frontend surfaces in React and TypeScript
- Build and maintain evaluation harnesses and testing frameworks that give the team confidence in AI feature quality before and after release
- Establish observability patterns for AI features, including latency tracking, error rates, model quality signals, and user feedback loops
- Validate and continuously improve AI-assisted development workflows, using tools like GitHub Copilot and Claude to accelerate team delivery
Platform Collaboration & Compliance Awareness
- Work closely with the AI Platform team to leverage shared infrastructure -- vector search, model gateways, prompt management services -- and surface requirements that should be addressed at the platform layer
- Apply secure-by-default design practices, including least-privilege access controls, audit logging, and encryption appropriate for systems handling financial member data
- Maintain working familiarity with data privacy and compliance expectations relevant to regulated financial services, enabling productive collaboration with compliance stakeholders
- Collaborate proactively with the Security team during feature design to ensure AI capabilities meet security requirements before implementation begins
Collaboration & Growing Others
- Develop Senior engineers toward Staff-level scope; give them problems and opportunities that stretch them, not just guidance on their current work
- Partner with Engineering Managers and Product leadership to align technical decisions with delivery goals
- Own the design and maintenance of technical knowledge infrastructure -- RFCs, ADRs, runbooks, onboarding paths -- so teams can operate without needing to escalate
Qualifications: Knowledge, Skills, and Abilities
Required
- 8+ years of professional software engineering experience, with demonstrated technical leadership across multiple teams or product areas
- Proven ability to make and defend architecture decisions at scale
- Active daily use of AI-assisted development tools
- Bachelor’s degree in Computer Science, Software Engineering, or equivalent experience
- Demonstrated experience building and shipping customer-facing AI or LLM-integrated features in production environments
- Strong proficiency in Python for backend and service development, including RESTful API design with frameworks such as FastAPI or Django
- Hands-on experience with LLM integration patterns, including prompt engineering, context management, RAG pipelines, and provider APIs (e.g., OpenAI, Anthropic)
- Experience building and maintaining evaluation frameworks for LLM-based systems, including output quality testing and regression detection
- Solid working knowledge of modern frontend development (React, TypeScript) sufficient to contribute to and review AI feature surfaces
- Experience deploying and operating applications on AWS, including IAM, managed services, and cloud-native architecture
Preferred
- Prior experience building software in a financial services, fintech, or other regulated technology environment
- Familiarity with AI compliance and governance considerations applicable to financial institutions (e.g., model risk management, fair lending, NCUA guidance)
- Experience with vector databases and semantic search infrastructure (e.g., pgvector, Pinecone, OpenSearch)
- Working knowledge of AI evaluation tooling or experiment tracking frameworks (e.g., LangSmith, MLflow, Weights & Biases)
- Exposure to agentic workflow patterns, multi-step AI orchestration, or tool-use implementations
What Success Looks Like
A successful hire at this level establishes themselves quickly as the architectural voice for AI feature quality and delivery on the team. In the first few months, they are setting technical direction on active AI features, raising the evaluation bar so the team ships AI capabilities with confidence, and building a productive working relationship with the AI Platform team. Over time, their impact is measured in the quality and reliability of AI features reaching clients, the technical growth of the engineers around them, and how well the team’s AI architecture holds up as the product portfolio expands.
