Staff Software Engineer ( AI )

MeridianLink

1d•Remote

About The Position

As a senior individual contributor and one of the highest-ranking technical voices in our engineering organization, the Staff Software Engineer (Professional 5) plays a pivotal role in shaping our AI platform strategy. This role, part of the Platform Services team, is dedicated to designing and building foundational infrastructure to power MeridianLink’s expanding suite of customer-facing AI capabilities across our credit union software product lines. Rather than focusing solely on implementation, the Staff Engineer acts at the intersection of architecture, strategy, and hands-on technical delivery. You’ll own the technical direction for AI platform systems, establish standards and patterns, and lay the architectural groundwork to enable reliable, scalable, and responsible AI development. This engineer will report directly to the Director of Platform Services and exert significant cross-functional influence across multiple engineering teams. As our AI platform matures—from third-party LLM integrations toward custom model development—your responsibilities will evolve, giving you increasing ownership over our model infrastructure, evaluation harnesses, and data pipelines needed to support production-grade AI in a regulated financial environment. This fully remote position works within a sub-team in Platform Services, responsible for building and scaling MeridianLink’s first generation of customer-facing AI capabilities. The team closely partners with application engineering, cloud infrastructure, and applied AI, creating technological foundations vital for delivering intelligent features to our credit union clients.

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

Nice To Haves

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

Responsibilities

Define multi-team strategy, address architectural bottlenecks, and evaluate build vs. buy decisions across the platform.
Architect scalable AI systems with security, compliance, and developer experience at the core.
Lead cross-team architecture reviews, drive collaborative alignment, establish engineering best practices, and set the bar for design reviews and production readiness.
Leverage hands-on knowledge of LLM/GenAI application patterns (e.g., prompt management, RAG pipelines, evaluation harnesses) and design for safety, auditability, and compliance in financial services AI.
Mentor and sponsor engineers at L3–L4 levels, facilitate knowledge sharing, and drive tech debt and best practice initiatives for engineering excellence.
Author clear, auditable architectural documentation; translate technical concepts for business partners; and partner with Product on business requirements and roadmaps.
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.