AI Data & Architecture Specialist responsible for optimizing AI data pipelines at Straker. Designing retrieval systems for autonomous AI workforce management in New Zealand.
Responsibilities
Design and optimize Retrieval-Augmented Generation (RAG) pipelines
Own the selection and scaling of Vector Databases
Develop sophisticated metadata schemas to improve search relevance
Build ETL pipelines that turn messy, unstructured data into 'LLM-ready' formats
Lead the creation of synthetic datasets for agent training and benchmarking
Establish rigorous cleaning protocols
Evaluate and select embedding models for specific domains
Collaborate on model fine-tuning when off-the-shelf LLMs aren't enough
Continuously tune hybrid search to optimize performance
Requirements
Expert-level Python and SQL
Experience with orchestration tools like Airflow or Dagster
Deep experience with Vector DBs and similarity search algorithms (HNSW, IVF)
A strong grip on tokenization, embeddings, and semantic chunking
Experience managing large-scale data on AWS, GCP, or Azure
Experience with Graph Databases (e.g., Neo4j) for relationship mapping
A background in Information Retrieval (IR) or Library Science
Familiarity with AI data privacy frameworks (GDPR/NZ Privacy Act)
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