Engineering Lead – Founding Enginner

7 years

0 Lacs

india

Posted:5 days ago| Platform: Linkedin logo

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Skills Required

engineering ai development pipeline processing vision drive ranking stack next.js typescript postgresql cutting design architecture software react aws sqs optimization retrieval strategies query indexing filtering fusion evaluation metrics technology

Work Mode

Remote

Job Description

Engineering Lead - Agentic RAG Platform

Xenonlabs.AI | Remote | Equity + Revenue Sharing


The Opportunity

XeRAG


The platform is in an advanced stage of development, with roughly 70% of the core product already built.


What You'll Own

  • Architect and scale advanced RAG pipelines (hybrid search, re-ranking, agentic RAG)
  • Lead full-stack development: Next.js 14, TypeScript, PostgreSQL + pgvector, AWS
  • Implement cutting-edge techniques: HyDE, CRAG, Graph RAG, cross-encoder re-ranking
  • Design multi-tenant architecture with enterprise security
  • Drive product from advanced development to production launch

Requirements

  • 7+ years software engineering; 4+ years TypeScript/React/Next.js
  • Production RAG experience with vector databases and LLMs
  • Deep AWS knowledge (Lambda, S3, SQS, CDK)
  • PostgreSQL optimization expertise
  • Startup mindset — self-directed, comfortable with ambiguity


Advanced RAG Expertise (Required)

  • Retrieval Strategies: Hybrid Search: dense vectors + BM25/sparse + keyword
  • Multi-Vector Retrieval: document-level vs. chunk-level embeddings
  • HyDE (Hypothetical Document Embeddings), multi-query/expansion
  • Contextual compression and dynamic context pruning
  • Chunking & Processing: Semantic chunking, hierarchical indexing (parent-child)
  • Late chunking (embed full docs before splitting)
  • Metadata enrichment using document structure (headings, tables, sections)
  • Re-Ranking & Filtering: Cross-encoder re-ranking (Cohere Rerank, BGE-reranker or equivalent)
  • RRF (Reciprocal Rank Fusion), MMR (Maximal Marginal Relevance)
  • Pre/post-retrieval metadata filtering
  • Advanced Techniques: Agentic RAG with tool use and multi-step reasoning
  • Self-RAG, CRAG (Corrective RAG), Graph RAG, RAPTOR
  • Evaluation & Optimization: RAG evaluation metrics: faithfulness, relevance, context precision/recall
  • RAGAS or similar frameworks; offline/online eval pipelines
  • Prompt engineering for accuracy and grounding
  • Hallucination detection and mitigation


Ideal Candidate

You want to own and scale something already in motion. You're excited about RAG/AI technology and prefer equity upside over guaranteed salary. You thrive with autonomy.

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