AI Retrieval & Relevance Engineer (RAG/Hybrid search)
Tasks
- Architect RAG workflows
- Build retrieval filtering and boosting
- Create query sets and golden datasets
- Define retrieval evaluation metrics
- Design indexing and ingestion pipelines
- Design retrieval services for LLM agents
- Enrich metadata for retrieval
- Evaluate and integrate information retrieval research
- Implement chunking and embedding pipelines
- Implement hybrid dense sparse retrieval
- Implement query rewriting
- Implement tiered retrieval and fallbacks
- Instrument retrieval telemetry and dashboards
- Integrate LLMs with retrieval mechanisms
- Integrate knowledge graph aware retrieval
- Maintain retrieval model and pipeline documentation
- Manage index refresh and backfills
- Manage relevance judgments and benchmarks
- Monitor latency and cost
- Optimize caching and rate limiting
- Optimize ranking and reranking
- Orchestrate retrieval evaluation pipelines
- Version eval datasets
Perks/Benefits
- Dental care
- Excellent office facilities
- Free bike parking
- Great work-life balance
- Social package
- Special discounts
- Supplemental health care
- Team bonding events
Skills/Tech-stack
Airflow | Cloud Computing | Dense retrieval | Elasticsearch | Faiss | Fine Tuning | Hybrid search | Information Retrieval | Knowledge graphs | Kubeflow | LLM Fine-tuning | Learning-to-Rank | Machine Learning | Milvus | Pinecone | Prompt engineering | PyTorch | Python | Query Understanding | Ranking | Reranking | Retrieval-Augmented Generation | Spark | Sparse Retrieval | TensorFlow | Vector Search | Weaviate
Education
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