Mirrai Careers

Sr. AI Engineer - Engineering Enablement

MeridianLink

US Remote Remote Full-time$150k–$190k / year Posted 30+ days ago
Market rate. This role pays around the $187k median for similar USD roles (1271 comparable postings in our corpus).
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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 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.

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