Staff Software Engineer - AI Products

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.

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.

• 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

• Reasons fluently across the fundamentals of distributed systems: how systems handle load, how they store large datasets, and how they stay correct under failure

• Treats every architecture decision as a trade-off between speed, cost, and correctness, and makes that trade-off explicit when guiding teams; sizes systems with back-of-the-envelope estimation and decides microservices versus monolith on evidence rather than default

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

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.