MeridianLink

MeridianLink

AI Engineer II – Trust & Explainability (AI Platform)

US Remote · Mid

No sponsorshipDetected 24 days ago
TypeScriptPythonData StructuresAlgorithmsCode ReviewGitAWSAzureDockerLLMsRAGAI OrchestrationCybersecuritySOC OperationsTest AutomationCollaboration

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Lottery odds40
Fits your clock70

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About the role

  • 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.
  • This is a deeply technical, hands-on engineering role and an integral part of the platform itself.
  • You do not need to be an expert in all of it.

Responsibilities

  • Build tracing across the platform's gateway, orchestration, memory, and tool layers, following defined designs from the team lead and architects
  • Build the correlation that links actions across agents in a single multi-agent workflow, including handoffs, parallel branches, and retries
  • Build the developer-facing trace view that makes a full agent conversation readable to someone who did not write the agent
  • Build the explanation layer that turns trace data into a human-readable account of why an agent did what it did
  • Build the platform primitives product teams surface to end users: explanation records, confidence and provenance metadata, and "what the agent relied on" summaries
  • Partner with product engineers on the Document Request Agent and the MLM agents to land those primitives in real products
  • Build the gaps: the pieces of trust and explainability tooling the ecosystem does not provide yet

Requirements

  • 3+ years of professional software engineering experience, delivering features independently in a production environment
  • Proficiency with standard development tooling: Git, Docker, automated testing, and modern scripting languages
  • Bachelor's degree in Computer Science, Software Engineering, or equivalent experience
  • Experience with distributed tracing or observability tooling in a production system
  • Experience running workloads on Azure or AWS, including the basics of identity and access management, networking, and secrets management
  • You should have real hands-on experience with LLM-based systems and a strong pull toward the problems of trust, tracing, and evaluation.
  • Solid understanding of algorithms, data structures, and software design fundamentals
  • Active daily use of AI-assisted development tools
  • 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
  • Strong automated testing instincts, including an interest in how to test systems that do not return the same answer twice

Nice to have

  • 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
  • Prior experience in financial services, fintech, or another regulated industry where explainability shaped technical decisions
  • Completes assigned features and bug fixes independently, with limited need for day-to-day guidance
  • Participates in code review and provides constructive feedback
  • Surfaces blockers proactively rather than waiting for check-ins

Skills

  • L2 AI Engineers at MeridianLink are active, daily users of AI-assisted development tools.

Company info

  • About the Opportunity This is a deeply technical, hands-on engineering role and an integral part of the platform itself.
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