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.