HRMS Tech Lead

Lead the technical delivery of an enterprise HRMS platform built on Frappe/ERPNext. Responsible for system architecture, hands-on backend and frontend development, and managing the DevOps pipeline. Works alongside an AI-assisted development workflow (Kiro IDE) to guide code generation, review output, enforce architectural standards, and ensure production-grade quality. Serves as the bridge between product requirements and working software.

 Required competencies & skills:

• 10+ years in full-stack development with at least 4 years in a lead/senior role
• Proven ability to design loosely coupled, modular architectures
• Experience managing Docker-based development and deployment environments
• Track record of setting up and maintaining CI/CD pipelines
• Ability to define and enforce engineering standards across a team
• Strong code review discipline with focus on security, performance, and maintainability
• Comfortable making pragmatic trade-offs between architectural purity and delivery speed
• Able to write production code hands-on to resolve issues that AI-assisted tooling cannot solve independently — including debugging complex edge cases, fixing looped failures, and providing reference implementations
• Self-directed, async-first communication style.

Technical & Functional Skills

• Python 3.11+, Frappe Framework, MariaDB, Redis, REST API design
• Vue 3 (Composition API), Vite, Tailwind CSS, vue-router
• Docker Compose, GitHub Actions, shell scripting, Linux administration
• Database schema design, query optimization, Frappe QueryBuilder, migrations
• Security: CSP policies, session/CSRF auth, secrets management, OWASP basics
• Testing: pytest, Hypothesis (property-based testing), integration testing
• Architecture: service boundaries, abstraction layers, event-driven design, loose coupling patterns
• Nice to have: ERPNext/Frappe internals, AWS (ECS, RDS, CloudWatch), Terraform, enterprise HRMS domain knowledge

Key Competencies

• Architectural Ownership — Enforces loose coupling, clear service boundaries, and abstraction layers; prevents coupling debt from accumulating
• Delivery Focus — Drives module-by-module delivery with full validation cycles; doesn't ship untested code
• DevOps Mindset — Treats infrastructure as code; automates builds, deployments, and verification
• Quality Gatekeeping — Reviews AI-generated and human-written code against security, performance and engineering standards
• Pragmatic Decision-Making — Balances long-term architecture goals with short-term delivery needs; knows when "good enough" is appropriate
• Technical Communication — Documents decisions clearly; writes concise PR descriptions; provides actionable code review feedback
• Problem Diagnosis — When something breaks, identifies root cause rather than patching symptoms; explores fundamentally different approaches after two failed attempts
• Hands-On AI Collaboration — Steps in to write code directly when the AI agent enters a failure loop or cannot resolve a complex issue; unblocks the AI-assisted workflow by providing working reference implementations, fixing edge cases, or refactoring problematic patterns that the AI struggles to get right independently