Senior Gen AI specialists

Doaz | Remote (Global, KST ±5h preferred) | 30~40 LPA

ConGPT

auditable, evidence-linked decisions

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.

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.

Gemma-3 27B-VL

passed the (initial) resume screening

1. Technical Screen (45m):

   Your RAG decisions; live triage of a retrieval accuracy issue.

2. System Design (2h):

   End-to-end design for a compliance-grade knowledge system (data → RAG → UI/UX).

3. Take-Home (48h window):

   See “One Question to Answer” below.

4. 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:

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.

• 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: