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