Staff Software Engineer - AI Products

About this role

Position Summary

This role is the technical lead for MeridianLink’s customer-facing AI product engineering. The Staff Software Engineer - AI Products sits on our AI Products team and owns the architecture and delivery of intelligent features going directly into the hands of credit union clients. This is the first generation of AI-native products at MeridianLink, and this engineer sets the technical bar for how those products are built: from feature architecture and LLM integration patterns to evaluation quality and production reliability. They partner closely with the AI Platform team to leverage foundational infrastructure and with Product Management to translate business intent into well-designed AI features.

Key Competencies

What it means to be a Staff Engineer at MeridianLink

Staff engineers operate across multiple teams or an entire product line. They set technical direction, make architecture and technology decisions that others build against, and raise the engineering floor across the teams they touch. Staff engineers at MeridianLink are active, daily users of AI-assisted development tools -- and go further by building the workflows, tooling, and patterns that make those tools more effective for the teams around them.

Technical Leadership & Architecture

- Makes critical architecture and design decisions that span multiple teams or an entire product area

- Evaluates technology choices with a clear view of trade-offs at scale, not just for the immediate problem

- Drives technical standards and patterns that other engineers can follow without being supervised

- Identifies systemic problems before they become incidents

Cross-Team Execution

- Provides day-to-day technical direction for one or more scrum teams without holding a management title

- Steps into ambiguous, high-stakes technical problems across teams and drives them to resolution -- without being asked

- Holds a high bar in code and design review across team boundaries

AI Feature Architecture & Quality

- Designs customer-facing AI features with reliability, correctness, and user trust as primary constraints

- Defines evaluation and testing standards for LLM-integrated systems, including prompt regression testing, output quality metrics, and human evaluation criteria

- Architects AI features to degrade gracefully when model outputs are low-confidence or unexpected, maintaining a reliable user experience in production

- Balances AI capability decisions against compliance constraints relevant to regulated financial services

AI Product Engineering

- Applies deep practical knowledge of LLM application patterns: prompt engineering, context management, RAG pipelines, agentic workflows, and provider integration

- Makes informed decisions about AI capability design: when to use retrieval vs. fine-tuning, when to call the model vs. use deterministic logic, and how to structure multi-step AI workflows

- Works fluently across the full stack of AI product delivery -- from backend LLM integration to the frontend surfaces users see

- Interfaces with the AI Platform team to consume shared infrastructure and feeds real-world product requirements back into platform prioritization

Product Partnership & Stakeholder Influence

- Partners with Product Management to translate business requirements and user needs into concrete AI feature designs, contributing technical feasibility while incorporating market and customer context

- Communicates architectural tradeoffs and product constraints clearly to non-technical stakeholders, including product leadership

- Produces RFCs and ADRs that capture durable decisions for AI features and serve as shared reference for future product work

- Shapes the roadmap of AI feature investment by surfacing technical risk, capacity constraints, and platform dependencies early

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

- De

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