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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior / Information Retrieval Engineer (AI/ML), Brand Concierge - **Company:** Adobe Inc. - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $211,800.0 - $306,625.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Automated Storage and Retrieval Systems, Cloud Computing, Information Systems, Data Deduplication, Information Engineering, Elasticsearch, Graph Database, Information Retrieval, Python (Programming Language), Knowledge Management, Machine Learning, Neo4j, Search Technologies, Data Processing, Data Ingestion, Large Language Models, Generative AI, Information Technology, HuggingFace, Performance Monitor, Machine Learning Operations, Software Version Control, Docker - **Published:** August 9, 2026 - **Apply:** https://www.wayup.com/i-j-Senior-Information-Retrieval-Engineer-AI-ML-Brand-Concierge-Adobe-630832521923396/ ## About the Role + 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 and 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) 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 ## 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 ensure consistency and reliability of retrieved content Reasoning Support + Design and iterate on context window strategies that improve LLM reasoning (e.g., adaptive injection, task-based retrieval) + Collaborate with prompt engineers and model developers to align retrieval outputs with downstream model behavior Performance Monitoring + Track key retrieval metrics such as accuracy, latency, and fallback rate + Implement caching, prefetching, and deduplication strategies to optimize system responsiveness ## Related Videos - [Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Building an AI-Ready Content Lake: Scaling RAG and Document AI Beyond Demos](https://www.wearedevelopers.com/videos/1977-building-an-ai-ready-content-lake-scaling-rag-and-document-ai-beyond-demos) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)