We are seeking a hands-on VP of Engineering—effectively our CTO—who stays close to the code and contributes technically on a regular basis. You should have deep experience in heavy industries (manufacturing, energy, utilities, oil & gas, defense) and a proven track record building and scaling high-fidelity data, simulation, or industrial AI platforms from zero to one, ideally in an early-stage startup.
You thrive making critical architectural decisions across the full stack (edge to cloud) while working side-by-side with a small, elite engineering team. You write code, review PRs, debug production issues, and guide implementation hands-on. This is not a pure management or strategy-only role—candidates who have drifted too far from technical work or come from large-org executive tracks without recent hands-on depth won't fit. Strong product intuition to turn real industrial problems into scalable solutions is essential.
What you'll do:
Own end-to-end platform architecture for our Industrial OS, covering edge connectivity (cameras via RTSP, vibration/thermal/time-series sensors, PLC signals over OPC-UA/Modbus), multimodal data ingestion and normalization, client-specific ontology (mapping assets, workflows, sequences, changeovers, operational context), cloud orchestration, distributed systems, and integrations with enterprise systems (ERP, CMMS, WMS, historians).
Lead technical strategy and key architecture decisions to deliver a reliable, scalable, secure platform built for mission-critical industrial use—supporting real-time video + sensor + PLC fusion, sequence state tracking, cross-sensor anomaly detection, dynamic schedule replanning, and high uptime in discrete manufacturing and warehousing environments.
Stay hands-on in the codebase regularly: personally implement/refactor core pieces (e.g., video processing pipelines, edge feature extraction, ontology tools, inference adapters for object detection/forecasting/anomaly scoring), debug live issues across streaming multimodal data, pair with engineers on agent logic, run design reviews, and maintain high technical standards across Python/TypeScript.
Build, mentor, and lead a high-performing engineering team in a flat, accountable organization—while continuing to serve as the senior technical contributor who sets the quality bar and drives execution speed.
Evolve the platform's shared intelligence layer—capturing reusable asset templates, failure signatures, and operational outcomes from every deployment (with privacy-safe anonymization and review controls)—so new customer onboardings get faster over time without rebuilding from scratch.
Partner closely with product and founders to turn factory-floor realities into a clear technical roadmap and manage the full lifecycle: from early prototypes to production agents for Vision Quality (real-time operator conformance checking with alerts and video evidence), Maintenance & Anomaly (predictive failure detection with multimodal signatures and proactive work orders), and Operations Planning (live schedule monitoring, shortfall detection, and replanning proposals with human approval).
Represent the company's technical vision externally: demo live agent capabilities to customers and investors (e.g., real-time alerts with video clips or sensor evidence, auto-generated work orders, schedule recovery options), speak at industry events, and build credibility around ontology-grounded agents that improve with every deployment.
About Serious AI
Serious AI is an AI and automation company focused on solving complex problems in heavy industries such as utilities, oil & gas, logistics, and manufacturing. The company builds end-to-end AI platforms, custom models, and enterprise AI strategies that help organizations optimize operations, reduce downtime, and improve efficiency. Its solutions include predictive maintenance, supply chain optimization, fleet management, and outage response systems powered by advanced data and machine learning. Serious AI aims to “rebuild the industrial base” by combining deep industry expertise with cutting-edge AI engineering to deliver measurable operational outcomes for large enterprises.
