Principal AI Data Strategy Consultant (Relational Database, API Layers)
At Franklin Templeton, we believe success is built through powerful partnerships. As a forward‑thinking asset manager, we build dynamic relationships with clients, understand their goals, and navigate complex markets together. We leverage cutting‑edge strategies and deep insights to unlock opportunities for long‑term wealth creation. Our talented, global teams bring expertise that is both broad and unique.
From our welcoming, inclusive, and supportive culture to our globally diverse business, we offer opportunities not only to help you reach your potential, but also to contribute to our clients’ success.
Franklin Templeton is building next-generation AI capabilities to power Sales Assist—a platform designed to enhance and personalize sales and client engagement. At the core of this initiative is a robust, scalable, and production-grade data foundation.
Role Summary
We are seeking a Principal AI Data Strategy Consultant to lead the design, build, and operation of AI-focused data systems. This role combines deep technical ownership of modern data architectures (vector databases, blob storage, RDBMS, APIs) with team leadership, ensuring reliable, secure, and high-performance data platforms that power AI applications in production.
How You Will Add Value?
Key Responsibilities
AI Data Platform Ownership
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Own the end-to-end architecture, implementation, and operation of data platforms supporting AI use cases, including:
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Vector databases for embeddings and semantic retrieval
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Blob/object storage for unstructured data (documents, transcripts, multimedia)
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Relational databases (SQL) for structured and transactional data
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API layers (GraphQL/REST) for data access and orchestration
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Ensure all data systems are production-ready, with high availability, scalability, and performance.
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Define standards for data storage, indexing, retrieval latency, and cost optimization across AI workloads.
Vector & AI Data Systems
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Lead the design and management of vector database ecosystems to support RAG and LLM-driven applications.
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Define strategies for embedding pipelines, chunking, indexing, and hybrid search (vector + keyword/metadata).
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Optimize retrieval quality, latency, and relevance for AI-driven sales insights.
Data Engineering & Storage Architecture
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Architect and oversee data pipelines that ingest, transform, and synchronize data across blob storage, warehouses, and vector stores.
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Establish patterns for multi-modal data handling (text, PDFs, structured data, CRM records).
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Ensure interoperability between enterprise data platforms (e.g., Snowflake, Databricks) and AI-specific storage systems.
API & Data Access Layer
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Define and implement GraphQL and REST API strategies to expose data services for AI applications like Sales Assist.
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Build and govern secure, scalable API layers that support real-time inference and retrieval workflows.
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Standardize schema design, versioning, and access control for...