Sr. Software Engineer - Engineering Enablement

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