cerebre

Senior AI Engineer (Generative AI / LLM Systems)

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SeniorHybryda·PoznańFull-time

#361547·Dodano 5 miesięcy temu·3

Źródło: cerebre

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Tech Stack / Keywords

AIGenerative AI LLM PythonBackendPyTorch TensorFlowCloud

Firma i stanowisko

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 integrate advanced AI capabilities directly into this platform, enabling natural language interaction with facility data, graph-aware reasoning over engineering systems, and AI-driven workflows across diagrams, documentation, and operational processes.

Wymagania

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 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.

Obowiązki

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, building chat-based experiences for users to explore facility systems, navigate diagrams, and query 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 interactions are observable, reliable, and production-safe.
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 identifying and addressing gaps in data, ontology, and system design.

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