Original listing text, shown exactly as published by the company.
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.
