AI Engineer - Trust & Explainability at MeridianLink

Confirmed open

MeridianLink is hiring a ai engineer - trust & explainability focused on AI platform trust and explainability.

MeridianLink

Location: Remote
Advertised region: US Remote / United States
Employment: FullTime
Work arrangement: remote
Technologies: Python, TypeScript, AWS, Azure, Docker

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Role overview

AI Engineer – Trust & Explainability (AI Platform)

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.

Responsibilities

Design, build and operate software or platform systems for AI platform trust and explainability.
Collaborate with product, engineering and customer stakeholders.
Improve reliability, performance, testing, automation and documentation.
Translate complex requirements into maintainable production systems.

Qualifications

3+ years of professional software engineering experience, delivering features independently in 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
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

Technology in this role

Python: Python TypeScript AWS Azure .
TypeScript: Python TypeScript AWS Azure .
AWS: Python TypeScript AWS Azure .
Azure: Python TypeScript AWS Azure .
Docker: Python TypeScript AWS Azure .
Go: Engineers who thrive here like the intersection of AI and rigor, want to become the team's go-to on agent tracing and explainability, and are motivated by making AI something people can see into and .
LLM: 3+ years of professional software engineering experience, delivering features independently in a production environment Solid understanding of algorithms, data structures, and software design fundamentals Proficiency with standard development tooling: Git, Docker, automated .
large language models: 3+ years of professional software engineering experience, delivering features independently in a production environment Solid understanding of algorithms, data structures, and software design fundamentals Proficiency with standard development tooling: Git, Docker, automated .
RAG: 3+ years of professional software engineering experience, delivering features independently in a production environment Solid understanding of algorithms, data structures, and software design fundamentals Proficiency with standard development tooling: Git, Docker, automated .
Git: 3+ years of professional software engineering experience, delivering features independently in a production environment Solid understanding of algorithms, data structures, and software design fundamentals Proficiency with standard development tooling: Git, Docker, automated .
CAN: • 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 .

Benefits

Location and remote arrangement as stated in the official posting.
Opportunity to work on mapping, financial technology or AI platform products.

About MeridianLink

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.

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

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

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

Employer website

How to apply

Review the original posting and apply through the employer’s careers page.

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Source checked: 2026-10-17

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