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.
Key Competencies
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.
Technical Execution & Delivery
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Owns features and infrastructure end-to-end: design through production release, limited guidance required
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Identifies edge cases and failure modes independently within assigned scope
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Participates actively in code review with constructive, specific feedback
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Surfaces blockers early rather than waiting for check-ins
Craft & Professionalism
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Writes tests that catch regressions without over-engineering the suite
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Monitors shipped work, responds to issues, and follows incidents to resolution
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Puts institutional knowledge into shared systems rather than individual heads
CI/CD & Build Systems
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Designs pipeline abstractions (templates, shared jobs, reusable configs) that work across multiple teams and tech stacks
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Reasons clearly about the tradeoffs between standardization and flexibility at org scale
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Keeps pipelines healthy, observable, and continuously improving
AI Tooling & Developer Infrastructure
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Builds and maintains shared MCP servers, agent orchestration harnesses, and reusable skills and plugins
Owns the shared infrastructure layer for autonomous AI agent environments: orchestration, provisioning, observability, cost controls, and security guardrails
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Partners with product teams on their individual sandbox configs while maintaining the platform underneath
Enablement & Engineering Advocacy
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Treats engineers as customers: office hours, documentation, feedback loops
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Measures platform impact with DORA metrics, adoption rates, and time-to-productivity data
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Closes the gap between shipping tooling and driving adoption
Expected Duties
CI/CD Platform
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Own and evolve shared infrastructure: templates, shared jobs, abstractions, and standards across R&D
Build reusable harness skills, slash commands, and workflow scripts that ship as internal plugins
Sandbox Infrastructure
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Own the shared infrastructure for AI agent sandbox environments: container orchestration, environment templates, networking, resource management
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Build and maintain orchestration and admin tooling: provisioning, lifecycle management, health monitoring, cost tracking
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Implement security guardrails for data isolation between sandbox environments
Enablement & Adoption
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Drive AI tooling adoption through documentation, onboarding programs, office hours, and direct team engagement
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Maintain the internal best practices hub and AI development playbook
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Instrument platform usage and productivity metrics to measure whether investments are moving the needle
Collaboration & Growing Others
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Participate in design discussions and code reviews; give and receive feedback constructively
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Mentor other engineers on the team
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Contribute to documentation and onboarding materials that reduce tribal knowledge
Qualifications: Knowledge, Skills, and Abilities
Required
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5+ years of professional software engineering experience, delivering features and infrastructure independently in production
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Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins
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Experience building developer-facing tooling or platform services other engineers depend on
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Hands-on experience with LLM developer tooling: MCP, LLM APIs, agent orchestration, or AI harnesses (Claude Code, Cursor, Copilot Workspace, or equivalent)
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Deep proficiency in Python or TypeScript, with production experience sufficient to own and deliver real features
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Proficiency with Kubernetes and Helm at production scale on AWS or Azure
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Experience designing shared pipeline abstractions and CI/CD infrastructure used by multiple teams
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Familiarity with infrastructure-as-code tools (Terraform, Pulumi, or equivalent)
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Proficiency with standard development tooling: Git, Docker, automated testing, and modern scripting languages
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Active daily use of AI-assisted development tools
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Bachelor’s degree in Computer Science, Software Engineering, or equivalent experience
Preferred
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Prior Engineering Enablement, Platform Engineering, or Developer Productivity role with direct measurement of developer velocity
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Experience building MCP servers or tool-integration layers for LLM-based systems
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Experience building or operating infrastructure for autonomous AI agents: sandboxed execution, scheduling, observability, cost management
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Familiarity with DORA metrics and developer productivity instrumentation
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Experience with JFrog Artifactory, Nexus, or equivalent artifact management systems
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Prior experience in financial services, fintech, or a regulated technology environment
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Exposure to SOC 2 or similar compliance frameworks from an engineering perspective
What Success Looks Like
Within the first few months, a successful hire is shipping CI/CD improvements teams are actively using and contributing meaningfully to the AI tooling platform. Over time, success is adoption: more teams on shared infrastructure, faster delivery, less one-off tooling being built in isolation. Engineers who thrive here care about making other people more productive and find genuine satisfaction in watching adoption metrics climb.