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NEXTMOVEFDE careers · United States
Adobe

Senior / Information Retrieval Engineer (AI/ML), Brand Concierge

AI summary of the role

This role focuses on building and optimizing Retrieval-Augmented Generation (RAG) pipelines and semantic search systems to power context-aware LLMs for enterprise AI agents.

No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.

What you’ll do

  • Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant)
  • Build ingestion pipelines for structured and unstructured data, including document chunking, embedding generation, and metadata tagging
  • Fine-tune relevance scoring, reranking algorithms, and query understanding mechanisms to improve precision/recall
  • Create and maintain knowledge graphs to support context linking and disambiguation

What you’ll bring

  • 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 with familiarity in retrieval libraries (e.g., Haystack, LangChain, Elasticsearch, Milvus)
  • Proficiency with embedding models, vector similarity search, and document indexing

Technologies

RAG · FAISS · Weaviate · Pinecone · Qdrant · Haystack · LangChain · Elasticsearch · Milvus · Airflow · dbt · Docker

About Adobe

Builds creative, document, and enterprise customer-experience software, now embedding Firefly AI and agents across creator and marketer workflows.

Public · 5000+ people

Source and classification

Internal deployment & tooling · Evidence for this classification:

The Opportunity We are seeking a highly skilled Information Retrieval Engineer to lead the development and optimization of retrieval systems that power context-aware large language models (LLMs). This role focuses on building robust Retrieval-Augmented Generation (RAG) pipelines to ensure AI agents and applications have access to the most relevant, timely, and high-quality information. You'll work at the intersection of data engineering, machine learning, and knowledge management—enabling better reasoning, accuracy, and performance for enterprise-grade AI systems. What you'll Do RAG System Design Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant) Implement semantic search infrastructure and hybrid retrieval systems (semantic + keyword) Data Processing & Ingestion Build ingestion pipelines for both structured and
More from the job description

The Opportunity We are seeking a highly skilled Information Retrieval Engineer to lead the development and optimization of retrieval systems that power context-aware large language models (LLMs). This role focuses on building robust Retrieval-Augmented Generation (RAG) pipelines to ensure AI agents and applications have access to the most relevant, timely, and high-quality information. You'll work at the intersection of data engineering, machine learning, and knowledge management—enabling better reasoning, accuracy, and performance for enterprise-grade AI systems. What you'll Do RAG System Design Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant) Implement semantic search infrastructure and hybrid retrieval systems (semantic + keyword) Data Processing & Ingestion Build ingestion pipelines for both structured and unstructured data sources Implement document chunking strategies, embedding generation (e.g., OpenAI, Cohere, HuggingFace), and metadata tagging Retrieval Optimization Fine-tune relevance scoring, reranking algorithms, and query understanding mechanisms Develop techniques to improve precision/recall for specific business domains or user tasks Knowledge Enhancement Create and maintain knowledge graphs to support context linking and disambiguation Manage data freshness and version control to en [... source excerpt omitted ...] us) Proficiency with embedding models, vector similarity search, and document indexing Familiarity with cloud platforms and MLOps tooling (e.g., Airflow, dbt, Docker) Preferred Qualifications 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 About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings includ [... source excerpt omitted ...] ct. Learn more about Adobe life, including our values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create. Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more. Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disabi

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