Principal AI Engineer - Foundation Models & Agentic AI (Cyber-Physical Systems)

Principal AI Engineer

Build the AI that Protects the World's Critical Infrastructure

At Indurex, we're building the world's first Engineering Intelligence Platform for Cyber-Physical Systems (CPS). Our mission is to combine Artificial Intelligence, Cybersecurity, Functional Safety, and Engineering Intelligence into one AI-native platform capable of understanding industrial systems the way engineers do. We're building AI capable of reasoning about industrial processes, learning from engineering data, understanding safety functions, investigating cyber-attacks, and helping operators make critical decisions in real time. Following our successful investment round, we're expanding our AI team and are looking for exceptional AI researchers and engineers who want to build something truly unique.

As Principal AI Engineer, you will define the technical direction of Indurex's AI platform. You'll lead the design and development of our proprietary AI models, multi-agent architecture, reasoning engines, and domain-specific foundation models for Cyber-Physical Systems. This is a hands-on technical leadership role combining AI research, software engineering, and product innovation.

What You'll Build

• CPS foundation models: domain-specific models that understand industrial systems, safety functions, and OT cybersecurity, starting from fine-tuned open models and moving toward proprietary over time.

• A multi-agent platform: autonomous agents that investigate cyber incidents, reason over engineering data, and support operators making critical decisions in real time.

• An industrial knowledge graph: a living model of assets, systems, and their relationships that grounds the AI in the real structure of a plant.

• An AI reasoning engine: the core that connects models, agents, and the knowledge graph to answer complex engineering questions end to end.

• A synthetic data platform: physics-based simulation and scenario generation to train and evaluate models where real industrial data is scarce.

Responsibilities

• Own Indurex's AI technical direction, setting the model and system architecture and making the build-versus-adopt decisions that shape the platform.

• Design and build the domain-specific models hands-on, taking them from fine-tuned prototypes to reliable production systems.

• Architect the multi-agent system, defining how agents plan, reason, use tools, and investigate across engineering and security workflows.

• Develop the machine learning that lets the platform detect anomalies, predict failures, and trace incidents to root cause.

• Lead development of the AI platform, covering training, serving, evaluation, and observability in production.

• Establish engineering standards and run architecture reviews across the AI team, keeping quality and reliability high as the codebase grows.

• Mentor and develop AI engineers while scaling the team.

What We Offer

• Highly competitive salary

• Performance bonus

• Employee Stock Option Plan (ESOP)

• Personal development budget

• Conference attendance

• Opportunity to publish research

• Direct influence on product strategy

• Flexible hybrid working

• 10+ years of software and AI/ML engineering in Python, including building and operating AI systems in production.

• Deep experience designing, orchestrating, planning, and shipping multi-agent systems, with frameworks such as PydanticAI, LangGraph, CrewAI, AutoGen, or Semantic Kernel.

• Strong AI engineering skills building LLM-powered applications with RAG, tool use, memory, and rigorous eval harnesses.

• Skilled in LLM training and adaptation through fine-tuning, LoRA/QLoRA, and distillation, with platforms like Hugging Face.

• Experience training production machine learning models for time-series data, anomaly detection, and change detection.

• Experience with data infrastructure at scale, spanning streaming ingestion, distributed processing, and storage.

• Comfortable deploying AI in production with tools such as Docker and Kubernetes.

• Proven ability to set technical direction and lead complex AI initiatives across a team.

Bonus Experience

• Experience in Industrial AI, OT/ICS, Functional Safety, or Cybersecurity.

• Familiarity with industrial protocols (OPC UA, Modbus, IEC 61850), digital twins, or knowledge graphs.

• Experience optimizing and deploying models for edge, embedded, or real-time environments.