Principal AI Architect

Role Overview

• Architect Embedded Agent Cycles: 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
• Workflow Optimization: Identify and re-engineer high-impact engineering bottlenecks, deploying AI agents that act as functional members of the development team
• ROI & Performance Measurement: Establish a data-driven framework to measure the impact of AI on engineering velocity, error reduction, and resource optimization. You will be responsible for defining and reporting on clear ROI metrics for all AI initiatives
• Standardized Guardrails: Design and implement automated guardrails within the CI/CD pipeline to ensure AI-generated output meets Mainspring’s rigorous safety, security, and quality standards
• Responsible Agent Execution: 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
• Model Evaluation: Build rigorous benchmarking systems to evaluate model reliability and cost-effectiveness, ensuring a lean and high-performing AI stack
• Agentic Infrastructure: 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)
• Build vs. Buy Strategy: Act as the technical authority on whether to leverage third-party AI platforms or develop proprietary agentic tools tailored to our specific hardware-software engineering needs

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

Benefits

• pre-IPO stock options
• benefits