Senior AI Engineer (Generative AI / LLM Systems)

About Cerebre

Cerebre builds software that helps industrial companies understand and operate complex facilities. Our platform transforms engineering diagrams, operational data, and documentation into an ontology -driven knowledge graph, PlantGraph, that models equipment, instrumentation, flow, and process relationships across a facility.

This foundation enables engineers and operators to reason about industrial systems with greater clarity and speed. We are now integrating advanced AI capabilities directly into this platform, enabling natural language interaction with facility data, graph-aware reasoning over engineering systems, and AI-driven workflows that operate across diagrams, documentation, and operational processes.

Quick Overview

• Build production AI systems that reason over industrial knowledge graphs (PlantGraph)
• Work on LLMs, RAG, and agent-based systems solving real-world engineering problems
• Integrate AI with structured data, diagrams, and operational workflows
• Own complex, ambiguous problems end-to-end in a high-impact domain

What We’re Building

Cerebre is building an AI-native platform that helps industrial companies understand and operate complex facilities.

At the core is PlantGraph, an ontology-driven knowledge graph that models equipment, instrumentation, and process relationships across a facility from engineering diagrams (P&IDs), documentation, and operational data.

We’re now embedding AI directly into this system - enabling:

• natural language interaction with facility data
• graph-aware reasoning over engineering systems
• AI agents that operate across diagrams, documents, and workflows

This is not generic LLM work; it’s about making AI reliable, grounded, and usable in real-world engineering environments.

What You’ll Do

• Build AI Systems That Reason Over Structured Industrial Data

  Design systems that allow LLMs to interpret and reason over PlantGraph and its underlying ontology, combining graph queries, ontology structures, and engineering data into reliable, explainable outputs.

• Create Natural Language Interfaces Over Complex Systems

  Build chat-based experiences that allow users to explore facility systems, navigate diagrams, and query equipment and process relationships through conversation.

• Orchestrate AI Across Graphs, Documents, and Workflows

  Develop systems that combine:

• graph queries
• engineering documentation (P&IDs, procedures, LOTO, work orders)
• real-world operational context to enable accurate, traceable AI outputs.
• Enable AI Agents to Safely Interact with the Platform

  Design APIs and tools that allow AI agents to operate on PlantGraph and system capabilities, ensuring interactions are observable, reliable, and production-safe.

• Productionize AI Systems at Scale

  Turn prototypes into production systems:

• scalable APIs and services
• performance and cost optimization
• evaluation, monitoring, and reliability frameworks
• Own Ambiguous, High-Impact Problems

  Work across engineering, ML, and domain teams to define and solve complex problems, including identifying and addressing gaps in data, ontology, and system design.

Core Engineering Challenges

• Grounding LLMs in structured graph data
• Reliable agent workflows across multiple data sources
• Query optimization across graph + vector + document systems
• Ensuring correctness, traceability, and validation in AI outputs
• Building production-grade AI systems for real-world industrial use

Required Qualifications

• 5+ years in software engineering, ML engineering, or applied AI
• Experience building AI systems that combine structured data with LLMs
• Strong experience with RAG, embeddings, and retrieval systems
• Experience building production AI systems (not just prototypes)
• Strong Python and backend engineering experience
• Experience designing scalable APIs and services
• Ability to take ownership of complex, ambiguous problems

Preferred Qualifications

• Experience with LLM agents or tool-based AI systems that interact with external systems via APIs or structured tools, including familiarity with emerging standards such as MCP
• Knowledge graph or graph database experience
• Exposure to industrial systems, P&IDs, or engineering workflows
• Experience with PyTorch / TensorFlow Distributed systems / cloud infrastructure experience

Tech Stack

• LLMs: OpenAI, open-source models (Hugging Face)
• AI Frameworks: LangChain, LlamaIndex, MCP
• ML: PyTorch, TensorFlow
• Data: Vector DBs, FalkorDB (graph), hybrid retrieval systems supporting PlantGraph and structured reasoning over engineering data
• Backend: Python services & APIs
• Frontend: .NET-based applications

Why This Role

This is a chance to work on real AI problems that matter, not generic chatbots or isolated prototypes, but systems used to operate real-world infrastructure.

You’ll be building AI that:

• reasons over structured engineering systems
• integrates deeply into workflows
• must be correct, explainable, and production-ready

More about Cerebre

We are cross-functional collaborators.

We blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings

We are experienced.

We are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling

We are serious builders.

We develop our platforms using leading practices in IT/OT architecture, OT security, AI architecture, ML Ops, and Platform engineering