Description:

The role will be responsible for designing, building, governing, and operating the enterprise vector database platform used across AI and GenAI use cases.

The candidate must combine:
Hands-on vector database expertise preferably Oracle 26 AI / Milvus or any other vector database.
Production experience building RAG solutions
Robust data engineering fundamentals
Understanding of AI data governance, security, lineage, and operational controls
Ability to establish a current capability from the ground up

Key Responsibilities
Design and manage vector database architecture and retrieval services.
Build end-to-end RAG pipelines including ingestion, chunking, embedding, indexing, and retrieval.
Develop scalable data pipelines for structured and unstructured data.
Implement data quality, metadata, lineage, security, and access controls.
Optimize retrieval accuracy, performance, scalability, and operational monitoring.
Establish governance and reusable standards for enterprise AI use cases.

Required Skills & Experience
Hands-on experience with Vector Databases (e.g.,



Milvus, Oracle AI Vector Search, Pinecone, Weaviate, Qdrant, pgVector).
Proven experience delivering production-grade RAG solutions.
Solid Python, SQL, and Data Engineering expertise.
Knowledge of embeddings, semantic search, hybrid search, reranking, and LLM integration.
Experience with APIs, CI/CD, containers, and cloud/on-prem platforms.
Understanding of data governance, security, lineage, and audit requirements.

Preferred
Experience in banking or regulated industries.
Familiarity with LangChain, LlamaIndex, Databricks, Spark, Kafka, or Airflow.
Experience building enterprise-scale AI platforms from the ground up.

Experience
8+ years of total Experiance in Data & AI with 4+ years working on AI/ML data platforms, semantic search, vector databases, or GenAI solutions and 2+ years of hands-on experience designing and deploying production-grade RAG solutions.

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