Sr. Software Engineer - Engineering Enablement

Job description

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.