Technical Principal Engineer (AI Platform)

Overview

About The Role

Ai-First Engineering System

• Design and operate the AI engineering platform (orchestrator, agent coordination, review gates, deployment and rollback flows)
• Build and manage a fleet of coding, testing, review, and operations agents
• Establish a spec-driven development model translating product intent into machine-executable workflows
• Define governance, autonomy boundaries, and verification layers (tests, evals, tracing, canary releases)
• Continuously improve system performance, autonomy, cost efficiency, and reliability across the SDLC

Product & Technical Architecture

• Own end-to-end SaaS architecture (services, APIs, data models, infrastructure)
• Make fast, high-impact architectural decisions in a startup environment
• Define and implement AI as a core product layer, not a feature
• Design real-time systems including dashboards and event-driven pipelines

Hands-On Engineering

• Build production-grade systems directly and alongside AI agents
• Own frontend, backend, database design, and performance optimization
• Deliver scalable, reliable code across the full stack

Ai And Llm Layer

• Own the full AI stack from prototype to production
• Build and maintain RAG pipelines, vector databases, and LLM integrations
• Design prompt strategies and AI workflows for core use cases
• Evaluate and integrate emerging AI capabilities into production systems

Integration Layer

• Own integrations across external systems (IoT, smart locks, payments, government and regulatory APIs)
• Ensure integrations are reliable, observable, and scalable for future expansion

Engineering Leadership & Delivery

• Lead a small high-performance hybrid team (engineers + AI agents)
• Set engineering standards, run reviews, and ensure delivery under pressure
• Translate product requirements into structured, agent-executable work
• Build and drive an AI-native engineering culture

Infrastructure & Reliability

• Own cloud infrastructure, CI/CD pipelines, and deployment architecture
• Implement observability across systems: monitoring, alerting, tracing, cost and latency tracking
• Ensure production readiness, reliability, and operational transparency

Requirements

• 5-7+ years of software engineering experience, including hands-on technical leadership of engineering teams
• Proven 0 → 1 experience building systems, teams, and architecture from scratch (not within already scaled organizations)
• Strong backend engineering expertise (TypeScript / Node.js or equivalent), with ability to design and evolve scalable service architectures
• Solid frontend capability (React, Vue.js or equivalent), with ability to architect, review, and unblock teams when required
• Deep understanding of data architecture (PostgreSQL or equivalent), including multi-tenant systems, performance optimization, and access control models
• Ability to define, select, and evolve technology stacks based on product needs and constraints, not industry trends
• Full-stack capability when needed, with strong architectural judgment and clear trade-off decision-making
• Daily, production-grade use of agentic coding tools across real systems (not demos or experimental use cases)
• Proven experience shipping features where a significant portion of production code is AI/agent-authored under your direction
• Hands-on experience designing or significantly extending AI-driven engineering workflows, including multi-agent orchestration, spec-to-PR pipelines, automated review systems, or agent-based testing frameworks
• Production experience integrating LLM-based systems into user-facing products, including RAG pipelines, vector databases, prompt engineering, and evaluation frameworks