{"id":2115966,"url":"https://alion.io/job/cerebre-senior-ai-engineer-generative-ai-llm-systems","title":"Senior AI Engineer (Generative AI / LLM Systems)","company":{"id":4185225,"name":"cerebre","domain":"cerebre.io","url":"https://alion.io/company/cerebre","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Traffit","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":".NET","optional":true},{"name":"C#","optional":true},{"name":"Hugging Face","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenAI","optional":true},{"name":"Platform Engineering","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-06-02T11:20:37Z","employer_posted_date":"2026-06-02","last_verified_at":"2026-10-27T06:56:18Z","board_verified":true,"closed_at":null,"days_open":146,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":146},"description":"About Cerebre\nCerebre 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.\nThis 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.\nQuick Overview\nBuild production AI systems that reason over industrial knowledge graphs (PlantGraph)\nWork on LLMs, RAG, and agent-based systems solving real-world engineering problems \nIntegrate AI with structured data, diagrams, and operational workflows \nOwn complex, ambiguous problems end-to-end in a high-impact domain \nWhat We’re Building\nCerebre is building an AI-native platform that helps industrial companies understand and operate complex facilities.\nAt 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.\nWe’re now embedding AI directly into this system - enabling:\nnatural language interaction with facility data\ngraph-aware reasoning over engineering systems\nAI agents that operate across diagrams, documents, and workflows \nThis is not generic LLM work; it’s about making AI reliable, grounded, and usable in real-world engineering environments.\nWhat You’ll Do\nBuild AI Systems That Reason Over Structured Industrial DataDesign 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.\n\nCreate Natural Language Interfaces Over Complex SystemsBuild chat-based experiences that allow users to explore facility systems, navigate diagrams, and query equipment and process relationships through conversation.\n\nOrchestrate AI Across Graphs, Documents, and WorkflowsDevelop systems that combine:\n\ngraph queries \nengineering documentation (P&IDs, procedures, LOTO, work orders) \nreal-world operational context to enable accurate, traceable AI outputs.\nEnable AI Agents to Safely Interact with the PlatformDesign APIs and tools that allow AI agents to operate on PlantGraph and system capabilities, ensuring interactions are observable, reliable, and production-safe.\n\nProductionize AI Systems at ScaleTurn prototypes into production systems:\n\nscalable APIs and services \nperformance and cost optimization \nevaluation, monitoring, and reliability frameworks \nOwn Ambiguous, High-Impact ProblemsWork across engineering, ML, and domain teams to define and solve complex problems, including identifying and addressing gaps in data, ontology, and system design.\n\nCore Engineering Challenges\nGrounding LLMs in structured graph data \nReliable agent workflows across multiple data sources \nQuery optimization across graph + vector + document systems \nEnsuring correctness, traceability, and validation in AI outputs \nBuilding production-grade AI systems for real-world industrial use \nRequired Qualifications\n5+ years in software engineering, ML engineering, or applied AI \nExperience building AI systems that combine structured data with LLMs \nStrong experience with RAG, embeddings, and retrieval systems \nExperience building production AI systems (not just prototypes) \nStrong Python and backend engineering experience \nExperience designing scalable APIs and services \nAbility to take ownership of complex, ambiguous problems \nPreferred Qualifications\nExperience 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\nKnowledge graph or graph database experience \nExposure to industrial systems, P&IDs, or engineering workflows \nExperience with PyTorch / TensorFlow Distributed systems / cloud infrastructure experience\nTech Stack\nLLMs: OpenAI, open-source models (Hugging Face)\nAI Frameworks: LangChain, LlamaIndex, MCP \nML: PyTorch, TensorFlow \nData: Vector DBs, FalkorDB (graph), hybrid retrieval systems supporting PlantGraph and structured reasoning over engineering data\nBackend: Python services & APIs \nFrontend: .NET-based applications \nWhy This Role\nThis 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.\nYou’ll be building AI that:\nreasons over structured engineering systems \nintegrates deeply into workflows \nmust be correct, explainable, and production-ready \nMore about Cerebre\nWe are cross-functional collaborators.\nWe blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings\nWe are experienced.\nWe are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling\nWe are serious builders.\nWe develop our platforms using leading practices in IT/OT architecture, OT security, 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