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1 day ago
This is a challenging role that requires architecting industrial-grade AI systems to turn unstructured data into auditable, evidence-linked decisions. The ideal candidate will have experience building production AI/ML or search systems and possess strong systems thinking and data modeling skills.
Key Responsibilities- Design multimodal Retrieval Augmentation Graphs (RAGs) over various data sources, including PDFs, CAD/vector drawings, incident reports, and regulations.
- Implement hybrid retrieval with reranking to achieve high accuracy and efficiency.
- Build domain embeddings for bilingual technical terminology and craft routing/prompting for audit-ready outputs.
- Ingest and normalize heterogeneous data sources, including ERP/MES exports, legacy DBs, SharePoint, and IoT streams.
- Ship 10k+ document-type ingestion with schema validation, redaction, and temporal versioning for regulatory drift.
- Integrate external APIs and orchestrate ensemble decisioning with SLA < 10s.
- 7+ years of experience building production AI/ML or search systems.
- Expertise in Python 3.11+, SQL, and strong systems thinking and data modeling.
- Experience with RAG at scale, including vector DBs, BM25, rerankers, prompt/routing strategies, and evals.
- MLOps/SRE skills, including versioning, canaries/A-B, drift detection, observability, cost/perf trade-offs.
- Knowledge graphs and schema alignment/ontologies.
- VL/Document AI and CAD/vector parsing.
- Orchestration frameworks and FastAPI.
- LLM fine-tuning/LoRA and retrieval-graded generation.
- Technical Screen (45m): Live triage of a retrieval accuracy issue.
- System Design (2h): End-to-end design for a compliance-grade knowledge system.
- Take-Home Challenge: Build a working RAG system that processes safety documents in Korean and English.
- Founder Conversation: Vision/values alignment and references with prior enterprise clients.
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