Full Stack AI Engineer

Full Stack AI Engineer

We’re looking for AI engineers who have been early joiners at VC-backed startups, building agentic, enterprise AI/data products from the ground up.

At SERIOUS AI, we are building physical AI agents for manufacturing and industry. Our agents optimize planning, maintenance and quality across discrete manufacturing and warehousing by ingesting multimodal sensor data, reasoning over structured operational knowledge and driving closed-loop decisions in real time.

You will join a team ABB, Palantir, Siemens, Volvo and leading AI startups.

What you have done

• Built and shipped agent systems in production with multi-step orchestration, tool use, state management and human-in-the-loop gates
• Worked with physical-world data at scale (RTSP video, time-series telemetry, vibration, thermal, PLC / OPC-UA) under real constraints (noise, drift, latency, alignment)
• Built multimodal AI pipelines combining vision (detection, segmentation, action recognition) with structured operational data
• Designed or contributed to ontology, knowledge graph or structured context systems grounded in real asset hierarchies and processes
• Integrated AI systems with ERP, CMMS, WMS, historians or PLC layers and handled normalization and schema mapping
• Shipped end-to-end systems from ingestion and model serving through backend services (Python, TypeScript) to frontend interfaces

What you will build

• Agent runtime and orchestration across Vision Quality, Predictive Maintenance and Operations Planning agents
• Context assembly from ontology and memory, tool dispatch via MCP, approval gates, tracing and token budget controls
• Multimodal sensor pipelines: video processing, YOLO / segmentation, FFT feature extraction, cross-sensor correlation (vibration, thermal, current)
• Industrial ontology / knowledge graph mapping plants, assets, sensors, work orders, materials and maintenance history
• Memory layer including trace storage, playbooks, asset templates and transferable failure pattern libraries
• Operational decision surfaces: dashboards, alerting workflows with evidence, replanning tools and audit trails
• Integration connectors across ERP, CMMS, WMS and PLC systems into a unified schema

Who you are

• Experience building AI systems from 0 to production, ideally as an early engineer at a high-growth startup
• Background in AI/ML applied to physical-world systems, with strong awareness of production constraints
• Deep understanding of data pipelines where ingestion, normalization, temporal alignment and ground truth are core challenges
• Thinks about agents as production systems, including failure modes, cost control and human interaction
• Comfortable working full stack across backend, data infrastructure, edge and frontend
• Strong preference for structured, grounded reasoning over naive retrieval approaches
• Motivated to work on real-world industrial problems with high ownership