MeridianLink
Staff Software Engineer - AI Products
US Remote · Staff+
Sponsorship not specifiedDetected 91 days ago
TypeScriptPythonReactDjangoFastAPIFull-Stack DevelopmentGitElasticsearchVector DatabasesAWSAPI DevelopmentRESTLLMsRAGAI OrchestrationAgileManual TestingLeadershipCollaboration
About the role
- 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.
- What it means to be a Staff Engineer at MeridianLink
- Staff engineers operate across multiple teams or an entire product line.
Responsibilities
- 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
- 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
- They set technical direction, make architecture and technology decisions that others build against, and raise the engineering floor across the teams they touch.
- Makes critical architecture and design decisions that span multiple teams or an entire product area
- Holds a high bar in code and design review across team boundaries
- 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
Requirements
- Knowledge, Skills, and Abilities
- 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
- Bachelor's degree in Computer Science, Software Engineering, or equivalent experience
- 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)
- 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
- 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)
- Active daily use of AI-assisted development tools
- Demonstrated experience building and shipping customer-facing AI or LLM-integrated features in production environments
- Experience building and maintaining evaluation frameworks for LLM-based systems, including output quality testing and regression detection
- Prior experience building software in a financial services, fintech, or other regulated technology environment
- Working knowledge of AI evaluation tooling or experiment tracking frameworks (e.g., LangSmith, MLflow, Weights & Biases)
Skills
- Evaluates technology choices with a clear view of trade-offs at scale, not just for the immediate problem
- Identifies systemic problems before they become incidents
- Provides day-to-day technical direction for one or more scrum teams without holding a management title
- Balances AI capability decisions against compliance constraints relevant to regulated financial services
Visa & Work Authorization
- Mentorship: Mentor and sponsor engineers at L3–L4 levels, facilitate knowledge sharing, and drive tech debt and best practice initiatives for engineering excellence
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