Staff Software Engineer ( AI )

MeridianLink is a company focused on building advanced AI capabilities for credit union software products. The Staff Software Engineer will play a key role in shaping the AI platform strategy, focusing on architecture, strategy, and technical delivery while mentoring other engineers and collaborating across teams.

Responsibilities:

• Own the reference architecture for the AI platform, encompassing API abstractions, prompt and version management, RAG infrastructure, vector retrieval, evaluation harnesses, and model serving
• Lead build-vs-buy evaluations for AI components, including due diligence and integration planning
• Design foundational data pipelines and infrastructure to support reliable, scalable AI services
• Define and document API contracts, standards, and platform interfaces across engineering
• Contribute to backend development in Python and RESTful API design, including integrations with leading AI providers
• Participate in full-stack delivery when needed, using React/TypeScript/Vite to deliver AI features
• Build and maintain tooling, SDKs, and platform APIs to enable safe, scalable AI adoption by product teams
• Drive improvements to AI-assisted development workflows with tools like GitHub Copilot and Claude
• Implement integrations with LLMs: context management, prompt templating, response validation, and robust fallback strategies
• Build and maintain evaluation frameworks for reproducible model testing
• Establish observability and performance tracking for AI services
• Participate in designing vector-based retrieval and model serving infrastructure as custom model capabilities become a focus
• Advise and collaborate with Security through architecture and design phases, ensuring compliance and security standards are met
• Apply secure-by-default principles (encryption, least privilege, audit logging) and stay current with financial data governance and privacy standards
• Collaborate with Product to translate business needs into technical designs and specifications, while contributing a strong architectural perspective
• Support AI platform roadmap development with product leadership
• Engage in companywide architectural forums and workgroups to drive engineering standards
• Partner with Data Engineering, DevOps/SRE, and Security to ensure aligned technical foundations
• Mentor L3–L4 engineers through code reviews, design sessions, and architectural guidance
• Promote engineering excellence, robust documentation, and reusable internal resources

Requirements:

• 8+ years in professional software engineering, with demonstrated cross-team architectural influence
• Proven end-to-end delivery of AI-powered features, from product concepts to production
• Experience leading large, complex technical initiatives
• Strong proficiency in Python and RESTful API development (FastAPI, Django)
• Knowledge of full-stack development (React, TypeScript)
• Hands-on integration with third-party LLM/AI APIs
• Experience with AWS (preferred), including IAM, networking, managed services, and storage
• Solid foundation in distributed systems, API design, cloud-native architecture
• Comfortable with CI/CD pipelines, version control, Docker, and AI-assisted development tools
• Demonstrated ability to author durable technical proposals (RFCs, ADRs)
• Clear communicator with technical and non-technical stakeholders
• GenAI experience (e.g., RAG pipelines, prompt management, LLM evaluation, agent frameworks)
• Familiarity with vector databases/semantic search (Pinecone, pgvector, OpenSearch)
• Exposure to model serving, MLOps/LLMOps practices, and model lifecycle/deployment tools
• Experience building developer platforms, SDKs, and self-service engineering tooling
• Experience with IaC tools (Terraform/Pulumi), Kubernetes, and observability tools (Prometheus, Grafana, Datadog)
• Leadership in technical communities, mentoring, and architectural standards
• Bachelor's in Computer Science, Software Engineering, or equivalent experience
• Experience building in financial services, fintech, or regulated environments
• Working familiarity with AI governance in financial services (e.g., NCUA, model risk, fair lending)
• Understanding of SOC 2 or similar compliance frameworks from an engineering perspective