What are the responsibilities and job description for the AI Platform developer/Architect position at Jobs via Dice?
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We are seeking a hands-on AI Platform Architect to design and scale an enterprise-grade AI-assisted software engineering platform. This role focuses on building standardized, secure, and extensible agent-driven workflows that transform the entire SDLC from design to operations into a repeatable and measurable system.
Key Responsibilities
We are seeking a hands-on AI Platform Architect to design and scale an enterprise-grade AI-assisted software engineering platform. This role focuses on building standardized, secure, and extensible agent-driven workflows that transform the entire SDLC from design to operations into a repeatable and measurable system.
Key Responsibilities
- Architect AI-powered SDLC workflows covering design, coding, testing, review, deployment, and operations
- Define reusable golden paths and agentic workflows (feature delivery, refactoring, incident response)
- Build and govern reusable playbooks/skill packs for standardized engineering practices
- Design a vendor-neutral extension system (commands, plugins, sub-agents, workflow hooks)
- Develop secure connector frameworks for integrating enterprise tools (CI/CD, repos, observability, APIs)
- Implement agent-driven automation for developer workspaces and operational tasks
- Deliver hands-on prototypes and production-ready platform components
- Define KPIs and evaluation frameworks to measure AI impact (productivity, quality, adoption)
- 8 years in software engineering, including architecture or platform leadership
- Strong expertise in SDLC, CI/CD, developer tooling, and distributed systems
- Hands-on experience with AI-assisted development workflows and agent orchestration
- Experience designing secure tool integrations and governed automation systems
- Proficiency in Python, TypeScript, or Java
- Ability to lead complex technical initiatives and deliver scalable platform solutions
- Background in platform engineering, DevEx, or internal tooling
- Familiarity with AI governance, security, and compliance
- Experience building evaluation frameworks for AI quality, safety, and reliability