AI Systems Architect

2 days ago

Kota, Rajasthan, India beBeeArchitecture Full time ₹ 80,00,000 - ₹ 1,00,00,000

Role Overview

  • We are seeking a seasoned expert to design and deploy cutting-edge AI systems that transform various data sources into auditable, evidence-linked decisions.

Key Responsibilities

  • The ideal candidate will architect industrial-grade AI systems that leverage multimodal RAG over PDFs, CAD/vector drawings, MSDS/chemicals, and bilingual regulations.
  • They will implement hybrid retrieval with reranking, targeting ≥90% top-k answerability and traceable citations.
  • The successful applicant will build domain embeddings for bilingual technical terminology and craft routing/prompting for audit-ready outputs.
  • They will ingest/normalize heterogeneous sources, shipping 10k+ document-type ingestion with schema validation, redaction, and temporal versioning.
  • The chosen professional will orchestrate ensemble decisioning with SLA < 10s, cost-optimized LLM flows (tool-use, caching, distillation).
  • Operational experience on AWS EKS, Postgres, Pinecone, Temporal/Argo, Prometheus/Grafana; CI/CD with test & eval gates is essential.

Requirements

  • A minimum of 7 years building production AI/ML or search systems, with 3+ enterprise deployments end-to-end.
  • Expertise in Python 3.11+, SQL, strong systems thinking, and data modeling is required.
  • RAG at scale: vector DBs, BM25, rerankers, prompt/routing strategies, evals (faithfulness, coverage, latency) expertise is necessary.
  • MLOps/SRE skills, including versioning, canaries/A-B, drift detection, observability, cost/perf trade-offs, are expected.

Nice-to-Have Skills

  • Knowledge graphs, schema alignment/ontologies, and VL/Document AI expertise are desirable.
  • CAD/vector parsing and safety/compliance domain exposure are highly valued.
  • Orchestration frameworks, FastAPI, AWS EKS, PostgreSQL, Pinecone knowledge is beneficial.

What We're Looking For

  • A sound architecture, multilingual retrieval quality, clean evidence chains, pragmatic cost/latency trade-offs, and a clear eval plan are essential.

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