Senior AI Engineer (Generative AI / LLM Systems)

Cerebre builds software that helps industrial companies understand and operate complex facilities. Their platform transforms engineering diagrams, operational data, and documentation into an ontology-driven knowledge graph called PlantGraph, modeling equipment, instrumentation, flow, and process relationships across a facility. They are integrating advanced AI capabilities directly into this platform to enable natural language interaction with facility data, graph-aware reasoning over engineering systems, and AI-driven workflows across diagrams, documentation, and operational processes.

• 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.

Nice to have:

• Experience with LLM agents or tool-based AI systems interacting 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, or cloud infrastructure.

• Build AI systems that reason over structured industrial data by designing systems that allow LLMs to interpret and reason over PlantGraph and its underlying ontology.
• Create natural language interfaces over complex systems, including chat-based experiences for exploring facility systems and querying equipment and process relationships.
• Orchestrate AI across graphs, documents, and workflows by developing systems combining graph queries, engineering documentation (P&IDs, procedures, LOTO, work orders), and real-world operational context.
• Enable AI agents to safely interact with the platform by designing APIs and tools ensuring observable, reliable, and production-safe interactions.
• Productionize AI systems at scale by turning prototypes into scalable APIs and services, optimizing performance and cost, and implementing evaluation, monitoring, and reliability frameworks.
• Own ambiguous, high-impact problems by working across engineering, ML, and domain teams to define and solve complex problems including gaps in data, ontology, and system design.