Senior / Information Retrieval Engineer (ai/ml), Brand Concierge

Adobe Adobe · Enterprise · San Jose, CA

Senior Information Retrieval Engineer focused on building and deploying scalable RAG pipelines and semantic search systems to power context-aware LLMs for enterprise AI applications. This role involves data processing, ingestion, retrieval optimization, and performance monitoring.

What you'd actually do

  1. Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant)
  2. Build ingestion pipelines for both structured and unstructured data sources
  3. Fine-tune relevance scoring, reranking algorithms, and query understanding mechanisms
  4. Design and iterate on context window strategies that improve LLM reasoning (e.g., adaptive injection, task-based retrieval)
  5. Track key retrieval metrics such as accuracy, latency, and fallback rate

Skills

Required

  • 4+ years in data engineering, ML infrastructure, or information retrieval
  • Experience building and deploying RAG pipelines or semantic search systems
  • Strong ML and Python skills
  • Familiarity with retrieval libraries (e.g., Haystack, LangChain, Elasticsearch, Milvus)
  • Proficiency with embedding models, vector similarity search, and document indexing
  • Familiarity with cloud platforms and MLOps tooling (e.g., Airflow, dbt, Docker)

Nice to have

  • Knowledge of graph databases (e.g., Neo4j, TigerGraph) or knowledge graph design
  • Experience optimizing retrieval for LLMs (e.g., OpenAI, Anthropic, Mistral)
  • Background in IR/NLP, Search Engineering, or Cognitive Computing
  • Degree in Computer Science, Information Systems, or a related field

What the JD emphasized

  • building robust Retrieval-Augmented Generation (RAG) pipelines
  • building and deploying RAG pipelines or semantic search systems
  • optimizing retrieval for LLMs

Other signals

  • building RAG pipelines
  • semantic search
  • vector databases
  • embedding generation
  • retrieval optimization for LLMs