Principal AI Architect

Mainspring Energy•Menlo Park, CA

10d•Hybrid

About The Position

Mainspring Energy is seeking a Principal AI Architect to lead the organization's transition into a company where AI agents are fully embedded in engineering development cycles. This strategic leadership role focuses on architecting the future of how linear generator technology is built. The Principal AI Architect will define the AI strategy for engineering development workflows, ensuring AI-driven automation has clear structure, rigorous guardrails, and measurable Return on Investment (ROI).

Requirements

12+ years of experience in software engineering or ML infrastructure, with a proven track record of moving AI beyond "chatbots" and into functional, automated workflows.
Deep Expertise in Agentic Frameworks: Proven track record in building autonomous systems using the Gemini and Anthropic SDKs (specifically via Vertex AI).
Expert-level proficiency in architecting agentic design patterns—including MCP (Model Context Protocol), multi-agent orchestration, and complex state management—within high-scale production environments.
Strategic ROI Focus: Ability to bridge the gap between technical execution and business value, with experience presenting ROI cases to executive leadership.
Security & Compliance Mindset: Expertise in building "safe-by-design" systems, specifically regarding data privacy and preventing model hallucinations in mission-critical engineering tasks.

Responsibilities

Transition Mainspring from "AI-assisted" tasks to a model where autonomous and semi-autonomous AI agents are natively integrated into the engineering lifecycle—from code generation and hardware simulation to automated testing and documentation.
Identify and re-engineer high-impact engineering bottlenecks, deploying AI agents that act as functional members of the development team.
Establish a data-driven framework to measure the impact of AI on engineering velocity, error reduction, and resource optimization, defining and reporting on clear ROI metrics for all AI initiatives.
Design and implement automated guardrails within the CI/CD pipeline to ensure AI-generated output meets Mainspringâ€™s rigorous safety, security, and quality standards.
Establish governance frameworks for agentic behavior, including "human-in-the-loop" checkpoints, audit logs, and access controls to prevent drift or unauthorized actions in production environments.
Build rigorous benchmarking systems to evaluate model reliability and cost-effectiveness, ensuring a lean and high-performing AI stack.
Design the end-to-end infrastructure required to support multi-agent systems, including orchestration layers, long-term memory (Vector DBs), and tool-use capabilities (RAG).
Act as the technical authority on whether to leverage third-party AI platforms or develop proprietary agentic tools tailored to specific hardware-software engineering needs.