Michael Saunders & Company

Compensation

Full Description

Job Description: • Design and implement data structures, embeddings, and taxonomies that enable efficient retrieval and contextualization of diverse datasets. • Develop and maintain pipelines for ingestion, transformation, enrichment, and indexing — ensuring data is clean, discoverable, and ready for AI consumption. • Build RAG (Retrieval-Augmented Generation) and semantic search pipelines using frameworks such as LangChain, LangGraph, or LangFuse, integrating structured and unstructured data. • Implement automated tagging, entity recognition, and classification pipelines using Python, ML, and NLP techniques. • Collaborate with AI and product teams to determine how insights should be surfaced and contextualized for “The Brain.” • Prototype, test, and deploy retrieval and intelligence systems that connect insights to natural language queries in real time. • Partner with engineers to integrate ML and retrieval systems into production APIs and applications. • Contribute to Suzy’s evolving data ontology and knowledge graph, defining how knowledge is linked across qualitative and quantitative sources. Requirements: • 5–10 years of experience in Machine Learning, Applied AI, or Data Engineering. • Strong Python expertise, with hands-on experience using Pandas, NumPy, scikit-learn, PyTorch, or similar frameworks. • Experience with LangChain, LangGraph, or LangFuse, and the ability to build and maintain RAG pipelines. • Experience with large-scale mixed datasets, including both quantitative (structured) and qualitative (textual, unstructured) data. • Deep understanding of embeddings, vector databases, and semantic search systems (e.g., FAISS, Weaviate, Pinecone, or Milvus). • Proficiency in data modeling, schema design, and ontology/taxonomy development for complex knowledge representation. • Hands-on implementation experience — capable of taking ideas from concept to working system. • Experience with SQL/NoSQL databases, data pipelines (Airflow, dbt, or similar), and API design for ML systems. • Comfort working in a fast-paced, experimental environment, balancing iteration with production readiness. • A builder’s mindset — curiosity, creativity, and a drive to make data smarter, more accessible, and more actionable. Benefits: