Principle Architect

Principle Architect

Job Responsibilities*

Work directly with customer engineering, product, architecture, and executive teams to identify SDLC bottlenecks and opportunities for AI-led acceleration.

Design and deliver working proofs using VelocityAI SDLC, modern AI development tools, agentic workflows, and automation.

Help customers move from traditional ticket- and sprint-based delivery toward intent-driven delivery loops that can generate plans, code, tests, documentation, and validation artifacts.

Define and measure acceleration outcomes such as reduced cycle time, faster story-to-code conversion, improved test generation, faster code review, reduced handoffs, and improved release readiness.

Present technical approach, architecture, proof results, business impact, and adoption recommendations to both technical and executive audiences.

Project Description

Must-Have Qualifications and Experience Experience:
15+ years of experience in software engineering, architecture, product engineering, platform engineering, solution architecture, or similar roles, with strong product-side delivery experience.

Domain Knowledge: Deep understanding of software architecture, SDLC, Agile/Scrum, DevSecOps, product lifecycle, and enterprise engineering practices.

Technical Fluency: Hands-on fluency with AI development tools, IDE-based AI assistants, agentic workflows, and AI-enabled software delivery, with the ability to build, evaluate, and explain working solutions.

Good-to-Have Skills or Attributes Tools & Frameworks:

Experience with tools and frameworks such as Claude Code, Google Cloud Code, OpenAI Codex, GitHub Copilot, Cursor, Windsurf, LangChain, AutoGen, LlamaIndex, Semantic Kernel, MCP, RAG, or vector databases.

Customer Engagement: Experience in customer-facing technical roles such as principal architect, field engineer, solution architect, forward-deployed engineer, technical advisor, or pre-sales engineering leader.

AI Governance: Strong understanding of AI governance, human-in-the-loop controls, responsible AI, security, observability, and production readiness for AI-enabled systems.