AI Engineer – Trust & Explainability (AI Platform)

AI Engineer – Trust & Explainability (AI Platform)

Job Summary

The AI Platform team is building the shared runtime that every AI agent at MeridianLink will run on as part of the MeridianLink One platform: model gateway, orchestration, memory, and observability, delivered as reusable services so product teams build agents once and run them safely for hundreds of credit union and lender customers. This role builds the trust and explainability layer of that runtime: the tracing, evaluation, and explanation capabilities that let engineers understand exactly what a multi-agent workflow did and why, and that let product teams show end customers the right level of explanation to trust an agent’s output.

This role has a strong dotted-line relationship to MeridianLink’s Security Operations team, so the platform’s trust guarantees and Security’s threat models are built from the same picture.

About the Opportunity

This is a deeply technical, hands-on engineering role and an integral part of the platform itself. You will write the code that traces agent actions across a multi-agent workflow, adopt and extend leading open-source explainability and observability frameworks, and build the primitives product teams use to surface explanations to lenders, credit unions, and their borrowers. You should have real hands-on experience with LLM-based systems and a strong pull toward the problems of trust, tracing, and evaluation. You do not need to be an expert in all of it. This is a place to build that expertise while the platform is being built, working alongside a Staff engineer team lead and architects who will invest in your growth.

What it means to be an L2 AI Engineer at MeridianLink

L2 AI Engineers handle a broad range of work independently, from bug fixes to feature development. They have developed a specialization and are deepening it. They write code that other engineers trust, ask good questions, and are starting to help the engineers around them. L2 AI Engineers at MeridianLink are active, daily users of AI-assisted development tools.

Technical Execution & Delivery

• Completes assigned features and bug fixes independently, with limited need for day-to-day guidance
• Designs components within a well-defined scope; escalates questions on complex system design rather than guessing
• Participates in code review and provides constructive feedback
• Surfaces blockers proactively rather than waiting for check-ins

Craft & Professionalism

• Writes tests that cover the functionality they ship
• Monitors and responds to issues with their own work
• Documents decisions and implementation details that others will need later

Agent Tracing & Explainability

• Understands how to instrument an agent so a single conversation can be followed from the first user message through every model call, tool call, retrieval, agent handoff, and decision to the final response
• Builds the correlation that ties actions across multiple agents in one workflow into a single readable trace
• Turns raw trace data into an explanation a human can follow: what the agent did, what it relied on, and why it chose that path

Customer-Facing Trust & Explanation

• Understands that the explanation an engineer needs and the explanation a borrower or loan officer needs are different, and builds for both
• Works with product engineers on what an agent should disclose at the product surface: what it did, what data it used, how confident it was, and what a human should verify
• Applies judgment about the right level of explanation for a regulated lending and account-opening context

Trust & Explainability Frameworks

• Familiar with the open-source landscape for LLM observability, tracing, and evaluation, and can evaluate a framework against the platform’s needs
• Integrates and extends existing frameworks rather than rebuilding them, and builds what does not exist yet
• Keeps the platform’s instrumentation aligned to emerging standards so traces stay portable across tools

Agent Evaluation & Quality

• Understands how to measure the quality of non-deterministic output: golden datasets, rubric and LLM-as-judge scoring, and regression baselines
• Builds evaluation checks that run in CI so a model swap, prompt change, or tool change is tested before it reaches customers
• Treats evaluation data as versioned, reviewed code

Safety & Tenant Isolation

• Knows the common attack patterns against LLM applica…