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, onboarding 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 onboarding 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 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.