- Vector & Graph ETL : Design and maintain pipelines that transform unstructured data (PDFs, emails, logs, chats) into optimized embeddings for Vector Databases (Pinecone, Weaviate, Milvus).

- Semantic Data Modeling : Engineer data structures that optimize for Retrieval-Augmented Generation (RAG), ensuring agents find the "needle in the haystack" in milliseconds.

- Knowledge Graph Construction : Build and scale Knowledge Graphs (Neo4j) to represent complex relationships in our trading and support data that standard vector search misses.

- Automated Data Labeling & Synthetic Data : Implement pipelines using LLMs to auto-label datasets or generate synthetic edge cases for agent training and evaluation.

- Stream Processing for Agents : Build real-time data "listeners" (Kafka/Flink) that feed live context to agents, allowing them to react to market or support events as they happen.

- Data Reliability & "Drift" Detection : Build monitoring for "Embedding Drift", identifying when the statistical distribution of your data changes and the agent's "knowledge" becomes stale.

Qualifications :

- Vector Database Mastery : Expert-level configuration of HNSW indexes, scalar quantization, and metadata filtering strategies within Pinecone, Milvus, or Qdrant.

- Advanced Python & Rust : Proficiency in Python for AI logic and Rust (or C++) for high-performance data processing and custom embedding functions.

- Big Data Ecosystem : Hands-on experience with Apache Spark, Flink, and Kafka in a high-throughput environment (Trading/FinTech preferred).

- LLM Data Tooling : Deep experience with Unstructured.io, LlamaIndex, or LangChain for document parsing and chunking strategy optimization.

- MLOps & DataOps : Mastery of DVC (Data Version Control) and Airflow/Prefect for managing complex, non-linear AI data workflows.

- Embedding Models : Understanding of how to fine-tune embedding models (e.g., BGE, Cohere, or OpenAI) to better represent domain-specific (Trading) terminology.

Additional qualifications :

- Chunking Strategy Architect : You don't just "split text." You implement Semantic Chunking and Parent-Child retrieval strategies to maximize LLM context relevance.

- Cold/Warm/Hot Storage Strategy : Managing cost and latency by tiering data between Vector DBs (Hot), SQL/NoSQL (Warm), and S3/Data Lakes (Cold).

- Privacy & Redaction Pipelines : Building automated PII (Personally Identifiable Information) redaction into the ingestion layer to ensure agents never "see" or "leak" sensitive user data.