Senior AI Solutions Architect — Enterprise Knowledge Systems Doaz | Remote (Global, KST ±5h preferred) | 30~40 LPA About Doaz Doaz builds domain-expert vertical AI for construction, heavy industry, and public safety. Our products— ConGPT (construction knowledge RAG), DGPT (multimodal quality-manual chatbot), PointChecker (contract/spec analysis), and GeoAI-Suite (borehole & excavation analysis)—help organizations like Doosan Enerbility, POSCO E&C, KT Estate, Lotte E&C , and Seoul Fire & Disaster HQ reduce risk, compress review cycles, and keep people safe. The Opportunity Architect industrial-grade AI that turns PDFs, CAD/vector drawings, incident reports, and regulations into auditable, evidence-linked decisions . You’ll own end-to-end systems (SaaS & on-prem) that prevent accidents, ensure compliance, and save lives —at Fortune-500 scale. What You’ll Do 1) Enterprise RAG & Knowledge Architecture Design multimodal RAG over PDFs (text/tables/images), CAD/vector drawings, MSDS/chemicals, and bilingual (KOR/ENG) regulations. Implement hybrid retrieval (BM25 + dense + metadata + knowledge graph) with reranking; target ≥90% top-k answerability and traceable citations . Build domain embeddings for bilingual technical terminology; craft routing/prompting for audit-ready outputs. 2) Industrial Data & Change-Aware Pipelines Ingest/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 ). 3) Production AI & MLOps Orchestrate ensemble decisioning (rules + priors + ML) with SLA < 10s ; cost-optimized LLM 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. 4) Vision & Document AI (Nice to have) Table/figure/annotation extraction (LayoutLMv3/Donut/DocFormer), symbol detection ( YOLOv8/RT-DETR ), PDF vector parsing. 5) Client Co-Creation Lead deep-dive workshops with CxO/stakeholders; design PoCs that land $1M+ programs. Mentor client teams and internal engineers; author crisp technical docs fit for audits. What You Bring Must-Have 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. RAG at scale: vector DBs ( Pinecone/Weaviate/FAISS ), BM25, rerankers, prompt/routing strategies, evals (faithfulness, coverage, latency). MLOps/SRE: versioning, canaries/A-B, drift detection, observability, cost/perf trade-offs. Clear communication; ability to turn messy, multilingual data into reliable software. Nice-to-Have Knowledge graphs ( Neo4j/RDF/SPARQL ), schema alignment/ontologies. VL/Document AI (LayoutLMv3, Donut), CAD/vector parsing, safety/compliance domain exposure. Orchestration frameworks (Temporal/Argo), FastAPI , AWS EKS , PostgreSQL , Pinecone . LLM fine-tuning/LoRA, retrieval-graded generation, multi-agent planning. Our Stack (you don’t need all of it) GPT-4o/GPT-4.1, Gemma-3 27B-VL , Qwen-VL , rerankers (Cohere/MXBAI), YOLOv8/RT-DETR, LayoutLMv3/DocFormer/Donut, FastAPI, Python, TypeScript, AWS EKS , Pinecone, Postgres, Temporal, Prometheus/Grafana. Interview Process passed the (initial) resume screening Technical Screen (45m): Your RAG decisions; live triage of a retrieval accuracy issue. System Design (2h): End-to-end design for a compliance-grade knowledge system (data → RAG → UI/UX). Take-Home (48h window): See “One Question to Answer” below. Founder Conversation: Vision/values alignment; references with prior enterprise clients. One Question to Answer (Take-Home Challenge), Challenge: Multi-language Safety Document RAG Prototype Goal: Build a working RAG system that processes safety documents in Korean and English , returning evidence-backed answers (required: inline citations and source snippets). Dataset Provided: 50 safety incident reports (25 Korean, 25 English) 10 regulatory PDFs (5 Korean KOSHA , 5 English OSHA ) 20 MSDS sheets (mixed languages) 10 sample queries with expected answers Deliverables (48h window): A running API (or CLI) that answers the 10 queries with citations Brief README covering: indexing strategy, retrieval pipeline (hybrid choices), chunking, reranking, bilingual handling, and evaluation method Report with metrics : answerability, faithfulness (citation match), and latency (p50/p95) What we’re looking for: Sound architecture, multilingual retrieval quality, clean evidence chains, pragmatic cost/latency trade-offs, and a clear eval plan. How to Apply Email: doaz@doaz.ai Subject: [Senior AI Architect – YOUR_NAME] Include: GitHub or repo for a production RAG/search system you built 1-page architecture diagram of your most complex deployed AI system Concrete metrics (accuracy, latency, scale, cost) (Bonus) Live demo URL, industrial AI write-ups, OSS contributions Why This Matters: The systems you build will directly influence real-world decisions at scale—improving compliance, reducing risk, and protecting workers every day.
Job description About Doaz Doaz is a hyper-growth startup on a mission to turn fragmented industrial knowledge into instant, actionable insight. We build LLM- and Vision-AI solutions for construction, heavy industry, and finance leaders who must transform terabytes of drawings, specifications, and regulations into real-time decisions. We’re expanding our GeoAI programs (including joint work with POSCO E&C) and launching drawing-change detection services that automatically compare plan versions, detect deltas, and explain design impacts. Why You’ll Love Working Here • True 0-to-1 ownership — Ship models that land in production sites within weeks. • Global impact, lean crew — 30 teammates across KR🇰🇷/PK🇵🇰/IN🇮🇳; no bureaucracy, only builders. • Tech freedom — YOLO or RT-DETR? Gemma-VL, Qwen-VL, or LLaVA? You choose, we fund. Role Overview We’re hiring a Senior Computer Vision & Multimodal LLM Engineer (GeoAI & Drawing Change Analysis). You’ll lead end-to-end development of a version-aware drawing-diff engine (PDF/DWG raster & vector), symbol/text extraction, and change-impact narratives powered by RAG/LLM. Expect fast cycles from prototype → service: detection models, OCR/layout understanding, retrieval, and explainable outputs that engineers can trust. Key Responsibilities Drawing Change Analysis (CV) Build a robust diff pipeline for architectural/structural/MEP drawings: rasterization, layer parsing, vector geometry ops, and semantic change clustering. Train/finetune detectors & segmenters (e.g., YOLOv8/RT-DETR/Detectron2/SAM) for symbols (columns, openings, sleeves), title blocks, and revision clouds; achieve production-grade mAP/F1. Implement geometry-aware post-processing (IoU/topology checks, snapping, graph connectivity) to reduce false positives. Document & Layout Understanding Engineer OCR + layout models (PaddleOCR/Tesseract + DocFormer/LayoutLMv3/Donut) to read legends, notes, schedules, and BOM tables; normalize to structured JSON. Build version-aware entity tracking (IDs, gridlines, BH IDs, coordinates) across revisions. GeoAI & LLM/RAG Design retrieval over drawings/specs (BM25 + vector) with reranking; ground LLM answers in evidence with citations and clickable locations. Generate change-impact summaries (e.g., slab shear reinforcement, opening proximity to columns) with rules + LLM verification; measure factual precision. Productization & DevOps Ship FastAPI/gRPC microservices, batch & streaming workers (Ray/Celery), GPU inference (Triton/TensorRT), and observability (Prometheus/Grafana). Own evaluation: dataset curation, data labeling guidelines, ablation/A-B tests, and regression suites. Collaboration Work closely with domain SMEs (geotech/structural) to encode rules (KDS/KBC, internal standards) and prioritize what matters to the field. Minimum Qualifications 5+ years of production Python (3.x) building ML-heavy backends; strong PyTorch. 3+ years in computer vision for detection/segmentation/OCR or document AI at scale. Hands-on with multimodal LLM/RAG (LangChain/LlamaIndex), vector DBs (Pinecone/Weaviate/FAISS), and rerankers. Proven experience parsing engineering drawings or complex PDFs (vector/raster), including geometry and layout reasoning. Solid MLOps: reproducible training, CI/CD, model packaging, monitoring; cloud on AWS/GCP. Fluent written & spoken English (Korean a plus). Preferred Extras GPU orchestration (Kubernetes/Ray/Slurm), high-performance inference (ONNX/TensorRT). Experience with VLMs (Gemma-VL, Qwen-VL, LLaVA), CLIP, or doc-layout models. Open-source contributions, papers, or strong public demos in CV/doc AI/RAG. Full-stack chops (TypeScript/Next.js/React) for quick operator tools and review UIs. Compensation & Benefits Competitive base salary (market-leading) , around 20 lakh (yearly) Performance-based annual bonus (up to 20%). cloud credits, and AI tools support. Hiring Process (≈ 2–3 weeks) Quick intro call (15 min, mutual fit). 48-hour take-home: Drawing Diff + Evidence-Grounded Summary (provide code + short README; clarity > polish). Deep-dive tech interview: architecture, modeling choices, evaluation, and scaling plan. Culture & vision chat with Founder/CEO. Offer — if all green, written offer within 24 h. How to Apply Email doaz@doaz.ai with subject [CV/LLM Engineer – Your Name] and include: Résumé/CV with measurable outcomes (metrics, latency, cost, accuracy). Current or recent salary. GitHub and/or live demos of CV/doc-AI/RAG work (links preferred). A one-page diagram of your “Drawing Revision → Detection → Evidence → LLM Narrative” pipeline, noting models, retrieval, and evaluation metrics. Employment type: Full-time Ready to turn messy drawings and specs into instant, trusted intelligence? Let’s build the future together at Doaz.
Senior/Lead UI/UX Designer (B2B SaaS · AI) — India (Remote) Company: Doaz (Seoul HQ · Global team) Compensation: ₹10–20 LPA (CTC), commensurate with experience Experience: 7+ years in product design / UIUX for complex, data-heavy products About Doaz Doaz builds vertical AI + SaaS solutions that solve hard business problems in construction, heavy industry, and enterprise workflows. Our products combine RAG/LLM , computer vision, and data-rich UIs to deliver real operational impact for large enterprises. The Role We’re hiring a top-tier Senior/Lead UI/UX Designer to own product experience end-to-end—research → IA → flows → wireframes → high-fidelity UI → prototypes → usability testing → design system. You’ll partner with founders, PMs, and engineers to ship clean, high-signal experiences that enterprise users love. What You’ll Do Turn ambiguous business problems into clear product flows and evidence-based designs . Design data-dense, workflow-heavy UIs (tables, forms, dashboards, multi-document viewers, analytics). Define and evolve our design system (tokens, components, accessibility, usage guidelines). Prototype AI-assisted interactions (RAG chat, explainability, evidence links, inline citations). Run user interviews and usability tests ; translate insights into measurable UX improvements. Collaborate tightly with engineering; ensure pixel-accurate, performant implementation. Track UX KPIs (task completion, time-to-decision, error rate) and iterate fast. Must-Haves 7+ years designing enterprise/B2B or systems-level products (SaaS strongly preferred). Portfolio with 2–3 shipped, complex products (not just marketing sites). Mastery of Figma/FigJam , design tokens, component libraries, and responsive layout. Proven chops with Material Design (Google), Apple HIG , and Salesforce Lightning Design System —knowing when to follow vs. adapt. Strong information architecture, interaction design, and micro-interaction skills. Accessibility mindset ( WCAG 2.1 AA ), internationalization, and data-table ergonomics. Comfortable collaborating across time zones; crisp written specs and handoff docs. Nice to Have Experience designing for AI features (RAG, copilots, chat, citations, confidence). Domain exposure: construction/engineering/EHS, GIS/CAD, fintech, or other data-intensive tools. Familiarity with front-end stacks (React, Tailwind, shadcn/ui) and Storybook. Experience setting up design QA and lighthouse metrics with PM/Eng. Why Doaz High ownership: Ship end-to-end and see your work used by major enterprises. Real impact: Design products that automate critical, high-stakes workflows. Senior team: Work with expert engineers and domain leaders across Korea, India, and Pakistan. Focus on craft: Clean, minimal, engineer-friendly UI. Depth over decoration. Location & Hours Remote (India) . Some overlap with KST (GMT+9) is expected. How to Apply (LinkedIn “Easy Apply” or link in your message) Please include: Resume/CV Portfolio link with case studies (must show problem → approach → outcome/metrics) Answers (short is fine): A. Show one example where you applied Material / Apple HIG / SLDS and what you adapted. B. Share a data-heavy screen you designed (table/dashboard/form) and how you reduced cognitive load. C. One measurable UX improvement you drove (baseline → result, e.g., time-to-task −40%). Doaz is an equal opportunity employer. We value skill, integrity, and outcomes. If you love building world-class enterprise UX—and you’re excited to design with and for AI—we’d love to meet you.