Senior Software Engineer, Engineering Enablement at MeridianLink

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MeridianLink is hiring a senior software engineer, engineering enablement focused on engineering enablement systems.

MeridianLink

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

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Role overview

This is a senior-level individual contributor on the Engineering Enablement team. The team builds the shared CI/CD infrastructure, AI development tooling, and sandbox environments that hundreds of R&D engineers depend on. A core part of that mission is advancing MeridianLink's AI-native development program — building the harnesses, agent infrastructure, and shared tooling that move engineering teams from ad-hoc AI usage toward autonomous, repeatable development pipelines. This role owns a significant chunk of that platform and drives adoption across engineering teams.

This is a hands-on role: real code, real infrastructure, direct engagement with engineering teams. The measure of success is how much faster you make everyone else.

What it means to be a Senior Engineer at MeridianLink

Senior individual contributors own their work end-to-end, identify problems before they're surfaced, and make the engineers around them better. Senior engineers at MeridianLink are active, daily users of AI-assisted development tools.

Owns features and infrastructure end-to-end: design through production release, limited guidance required

Identifies edge cases and failure modes independently within assigned scope

Participates actively in code review with constructive, specific feedback

Surfaces blockers early rather than waiting for check-ins

Writes tests that catch regressions without over-engineering the suite

Monitors shipped work, responds to issues, and follows incidents to resolution

Puts institutional knowledge into shared systems rather than individual heads

Designs pipeline abstractions (templates, shared jobs, reusable configs) that work across multiple teams and tech stacks

Responsibilities

Design, build or operate software and workflows for engineering enablement systems.
Collaborate with product, engineering, customer and operations stakeholders.
Improve reliability, performance, testing, automation and documentation.
Translate technical and customer requirements into maintainable systems.

Qualifications

5+ years of professional software engineering experience, delivering features and infrastructure independently in production
Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins
Experience building developer-facing tooling or platform services other engineers depend on
Hands-on experience with LLM developer tooling: MCP, LLM APIs, agent orchestration, or AI harnesses (Claude Code, Cursor, Copilot Workspace, or equivalent)
Deep proficiency in Python or TypeScript, with production experience sufficient to own and deliver real features
Proficiency with Kubernetes and Helm at production scale on AWS or Azure
Experience designing shared pipeline abstractions and CI/CD infrastructure used by multiple teams
Familiarity with infrastructure-as-code tools (Terraform, Pulumi, or equivalent)
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
Prior Engineering Enablement, Platform Engineering, or Developer Productivity role with direct measurement of developer velocity
Experience building MCP servers or tool-integration layers for LLM-based systems
Experience building or operating infrastructure for autonomous AI agents: sandboxed execution, scheduling, observability, cost management
Familiarity with DORA metrics and developer productivity instrumentation
Experience with JFrog Artifactory, Nexus, or equivalent artifact management systems

Technology in this role

Python: Python TypeScript AWS Azure Kubernetes .
TypeScript: Python TypeScript AWS Azure Kubernetes .
AWS: Python TypeScript AWS Azure Kubernetes .
Azure: Python TypeScript AWS Azure Kubernetes .
Kubernetes: Python TypeScript AWS Azure Kubernetes .
Docker: Python TypeScript AWS Azure Kubernetes .
R: The team builds the shared CI/CD infrastructure, AI development tooling, and sandbox environments that hundreds of R&D engineers depend .
LLM: 5+ years of professional software engineering experience, delivering features and infrastructure independently in production Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins Experience building developer-facing tooling .
Terraform: 5+ years of professional software engineering experience, delivering features and infrastructure independently in production Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins Experience building developer-facing tooling .
Git: 5+ years of professional software engineering experience, delivering features and infrastructure independently in production Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins Experience building developer-facing tooling .
Jenkins: 5+ years of professional software engineering experience, delivering features and infrastructure independently in production Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins Experience building developer-facing tooling .
CI/CD: The team builds the shared CI/CD infrastructure, AI development tooling, and sandbox environments that hundreds of R&D engineers depend .
Jira: • Build and maintain shared MCP server infrastructure connecting AI harnesses to internal systems (Jira, Confluence, GitLab, internal .
Confluence: • Build and maintain shared MCP server infrastructure connecting AI harnesses to internal systems (Jira, Confluence, GitLab, internal .

Benefits

US-remote arrangement as stated in the official posting.
Opportunity to work on AI and enterprise technology 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

Employer website

How to apply

Check the original posting to confirm the role is still open before applying.

Check employer posting

Source checked: 2026-10-17

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