Machine Learning Consultant

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: Apply tot his job