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.
Key Competencies
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: Active, daily users of AI-assisted development tools.
Expected Duties
CI/CD Platform: Own and evolve shared infrastructure: templates, shared jobs, abstractions, and standards across R&D; resolve systemic reliability issues; 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 & Engineering Advocacy: 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.
Qualifications
Knowledge, Skills, and Abilities: 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 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.
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.