AI Engineer II – Trust & Explainability (AI Platform)

The posting, as MeridianLink wrote it

AI Engineer II – 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 wo

[…]
n a production environment

• Solid understanding of algorithms, data structures, and software design fundamentals
• Proficiency with standard development tooling: Git, Docker, automated testing, and modern scripting languages
• Active daily use of AI-assisted development tools
• Bachelor's degree in Computer Science, Software Engineering, or equivalent experience
• Hands-on experience building software that integrates large language models (LLM APIs, agent frameworks, RAG pipelines, or similar), in production or in substantial personal or open-source projects
• Proficiency in Python with production experience; TypeScript a plus
• Hands-on experience in at least one of the following, with real interest in growing into the others: LLM observability and tracing, LLM evaluation and testing, agent frameworks and multi-agent orchestration, or application security testing
• Experience with distributed tracing or observability tooling in a production system
• Strong automated testing instincts, including an interest in how to test systems that do not return the same answer twice
• Experience running workloads on Azure or AWS, including the basics of identity and access management, networking, and secrets management

Preferred Qualifications

• Experience with OpenTelemetry, including the GenAI semantic conventions, or OpenLLMetry
• Experience with LLM observability and evaluation tools (Langfuse, Arize Phoenix, LangSmith, Braintrust, promptfoo, DeepEval, or equivalent)
• Contributions to open-source AI observability, evaluation, or agent framework projects
• Experience with cloud-managed model services such as AWS Bedrock or Azure OpenAI
• Experience building or operating multi-tenant SaaS systems where tenant isolation was a hard requirement
• Prior experience in financial services, fintech, or another regulated industry where explainability shaped technical decisions
• Experience building developer-facing debugging or visualization tools