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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 is