Brillius Technologies is building multiple AI-native products, including BrilliusLaw and BrilliusLearning
.
We are looking for an AI Engineering Platform Lead to create a common AI-assisted software development framework that can be used across all Brillius products.
— our persistent engineering and product knowledge system.
The role is inspired by modern agentic engineering approaches and the responsibility is to turn these concepts into a Brillius-owned development operating system covering product validation, UX/design, architecture, coding, reviews, testing, security, deployment and organizational learning.
This is not primarily a conventional application-development role
.
You will build the engineering system that helps the rest of the Brillius engineering team build applications faster and more consistently.
What You Will Build
You will own the creation of a AI Assisted Agentic reusable engineering platform covering:
- Product and feature validation workflows
- Product requirements and engineering planning
- UI/UX design and design-review workflows
- Architecture review
- AI-assisted coding workflows
- Code-review automation
- Security reviews
- Automated testing and QA
- Release and deployment workflows
- Production-readiness checks
- Engineering retrospectives
- Architecture decision records
- Organizational engineering memory
- Developer documentation and standards
The objective is to establish a repeatable flow such as: Requirement → Product Review → Design → Architecture → Build → Review → Security → QA → Ship → Learn Brillius Stack
You will create a standardized development framework that defines how software is built at Brillius
.
This includes
- Approved technology patterns
- Frontend/backend architecture standards
- API conventions
- Database standards
- Authentication and RBAC patterns
- Multi-tenant SaaS standards
- Coding conventions
- Testing standards
- Security practices
- Observability and logging
- DEV / UAT / PROD environments
- CI/CD and deployment
- Rollback and incident procedures
- Build-vs-buy decision frameworks
The platform should support vertical-specific extensions such as:
BrilliusLaw Stack
BrilliusLearning Stack while inheriting common Brillius engineering standards.
Brillius Brain
You will establish a persistent engineering knowledge layer that captures:
- Architecture decisions
- Product decisions
- Reusable engineering patterns
- Known issues and solutions
- Production incidents
- Security lessons
- Development standards
- Design decisions
- Technical research
- Build-vs-buy decisions
- Engineering retrospectives
The goal is simple: Solve an engineering problem once, and allow every future Brillius engineer and AI agent to benefit from that knowledge.
AI-Native Engineering
You should be highly comfortable working with modern AI coding and agentic-development tools.
We are looking for someone who understands that AI should accelerate engineering judgment—not replace engineering responsibility .
Team Enablement A significant part of this role is helping junior engineers become highly productive.
Many engineers in this team are early in their careers.
You will create guardrails that allow them to safely use AI for:
- Research
- Architecture
- Development
- Debugging
- Testing
- Documentation
- Deployment
You will also establish mandatory human-review gates for areas such as:
- Architecture
- Database changes
- Security
- Production deployment
Responsibilities
- Design and own the Brillius AI-assisted software-development platform.
- Extend and adapt open-source agentic engineering frameworks where appropriate.
- Build reusable AI Skills, agents, prompts and engineering workflows.
- Establish Brillius engineering standards and architecture patterns.
- Create vertical extensions for BrilliusLaw and BrilliusLearning.
- Build the Brillius engineering knowledge/memory system.
- Standardize testing, QA, security and release workflows.
- Improve developer productivity and reduce engineering rework.
- Mentor junior engineers in AI-assisted development.
- Review important architecture and infrastructure decisions.
- Establish production-readiness and observability standards.
- Continuously evaluate new AI developer tools and incorporate useful capabilities.
What We Are Looking For
Strong candidates will have
- 5–10 years of software-engineering experience
- Strong full-stack engineering fundamentals
- Experience designing scalable SaaS architectures
- Solid knowledge of APIs and relational databases
- Experience with cloud infrastructure such as AWS/Azure/GCP
- CI/CD and DevOps understanding
- Experience with authentication, authorization and RBAC
- Good understanding of software security
- Strong testing and QA discipline
- Experience reviewing architecture and production systems
- Ability to mentor junior engineers
AI Experience You should have hands-on experience with at least some of:
- Claude Code
- OpenAI APIs / Codex-style development tools
- Cursor
- GitHub Copilot
- Agentic coding frameworks
- AI workflow orchestration
- RAG / vector databases
- AI agents
- Prompt/Skill design
- LLM evaluation
- AI-assisted testing or code review
You do not need experience with every tool listed above. What matters is that you understand how to turn AI capabilities into a disciplined engineering workflow.
Preferred Technical Background
Experience with some of the following would be valuable:
You Will Be Successful If Within the first 3-4 months:
- Developers follow a consistent AI-assisted feature-development lifecycle.
- Architecture and coding standards are documented and enforceable.
- Junior engineers can build features faster with less rework.
- QA, security and deployment checks become systematic.
- Important engineering lessons automatically become reusable organizational knowledge.
Who This Role Is For This role may suit someone who has previously worked as a:
- Staff Software Engineer
- Principal Engineer
- Solutions Architect
- AI Engineering Lead
- Developer Platform Engineer
- Developer Experience Engineer
- Engineering Productivity Lead
- Full-Stack Architect
- AI Platform Engineer
You should enjoy building systems that help other engineers build better software , rather than only owning individual application features.
Why Brillius
We are building AI-native products across multiple professional verticals.
Our objective is not simply to add AI features into applications.
We want to create a development organization where: AI accelerates execution, engineering standards maintain quality, and organizational knowledge compounds over time.
You will have significant ownership in defining that engineering model.