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7 days ago
This role involves designing and developing industrial-grade AI systems that transform various forms of data into auditable, evidence-linked decisions.
Key Responsibilities:- Design multimodal Retrieval Augmentation Generator (RAG) over PDFs, CAD/vector drawings, MSDS/chemicals, and bilingual regulations.
- Implement hybrid retrieval with reranking; target ≥90% top-k answerability and traceable citations.
- Build domain embeddings for bilingual technical terminology; craft routing/prompting for audit-ready outputs.
- Ingest and normalize heterogeneous sources (ERP/MES exports, legacy DBs, SharePoint, IoT streams).
- Ship 10k+ document-type ingestion with schema validation, redaction, and temporal versioning for regulatory drift.
- Integrate external APIs (e.g., KOSHA, OSHA, EPA) for finance modules SEC/DART.
- Orchestrate ensemble decisioning with SLA < 10s; cost-optimized Large Language Model flows (tool-use, caching, distillation).
- Operate on AWS EKS, Postgres, Pinecone, Temporal/Argo, Prometheus/Grafana; CI/CD with test & eval gates.
- Build explanation layers (attribution, chain-of-evidence; SHAP/LIME where applicable) and human-in-the-loop feedback.
- Table/figure/annotation extraction (LayoutLMv3/Donut/DocFormer), symbol detection (YOLOv8/RT-DETR), PDF vector parsing.
- Lead deep-dive workshops with CxO/stakeholders; design Proof of Concepts that land $1M+ programs.
- Mentor client teams and internal engineers; author crisp technical docs fit for audits.
Requirements: 7+ years building production AI/ML or search systems; 3+ enterprise deployments end-to-end. Expert Python 3.11+, SQL; strong systems thinking and data modeling.
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