meridianlink - Sr. Software Engineer - Engineering Enablement
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Requirements
• 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 • 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.
Responsibilities
• 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
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