Sr. Software Engineer - Engineering Enablement

Position Summary

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

• 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

Craft & Professionalism

• 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

CI/CD & Build Systems

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

• Reasons clearly about the tradeoffs between standardization and flexibility at org scale

• Keeps pipelines healthy, observable, and continuously improving

AI Tooling & Developer Infrastructure

• Builds and maintains shared MCP servers, agent orchestration harnesses, and reusable skills and plugins

• Understands LLM developer tooling in practice: tool definitions, agent loops, prompt management

• Designs shared tooling with product thinking: requirements gathering, feedback triage, prioritized backlog

Sandbox & Agent Infrastructure

• Owns the shared infrastructure layer for autonomous AI agent environments: orchestration, provisioning, observability, cost controls, and security guardrails

• Partners with product teams on their individual sandbox configs while maintaining the platform underneath

Enablement & Engineering Advocacy

• Treats engineers as customers: office hours, documentation, feedback loops

• Measures platform impact with DORA metrics, adoption rates, and time-to-productivity data

• Closes the gap between shipping tooling and driving adoption

Expected Duties

CI/CD Platform

• Own and evolve shared infrastructure: templates, shared jobs, abstractions, and standards across R&D

• Resolve systemic reliability issues: flaky tests, slow builds, caching inefficiencies

• Partner with teams during migrations and help them adopt shared abstractions without disrupting delivery

AI Tooling Platform

• Build and maintain shared MCP server infrastructure connecting AI harnesses to internal systems (Jira, Confluence, GitLab, internal APIs)

• Develop agent orchestration infrastructure: scheduling, observability, cost controls, security boundaries

• Build reusable harness skills, slash commands, and workflow scripts that ship as internal plugins

Sandbox Infrastructure

• Own the shared infrastructure for AI agent sandbox environments: container orchestration, environment templates, networking, resource management

• Build and maintain orchestration and admin tooling: provisioning, lifecycle management, health monitoring, cost tracking

• Implement security guardrails for data isolation between sandbox environments

Enablement & Adoption

• Drive AI tooling adoption through documentation, programs, office hours, and direct team engagement

• Maintain the internal best practices hub and AI development playbook

• Instrument platform usage and productivity metrics to measure whether investments are moving the needle

Collaboration & Growing Others

• Participate in design discussions and code reviews; give and receive feedback constructively

• Mentor other engineers on the team

• Contribute to documentation and materials that reduce tribal knowledge

Qualifications: Knowledge, Skills, and Abilities

Required

• 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

Preferred

• 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

• Prior experience in financial services, fintech, or a regulated technology environment

• 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-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.

• 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
• Prior experience in financial services, fintech, or a regulated technology environment
• Exposure to SOC 2 or similar compliance frameworks from an engineering perspective