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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:

• Own the end-to-end architecture, implementation, and operation of data platforms supporting AI use cases, including:

  • Vector databases for embeddings and semantic retrieval

  • Blob/object storage for unstructured data (documents, transcripts, multimedia)

  • Relational databases (SQL) for structured and transactional data

  • API layers (GraphQL/REST) for data access and orchestration

• Ensure all data systems are production-ready, with high availability, scalability, and performance.

• Define standards for data storage, indexing, retrieval latency, and cost optimization across AI workloads.

Vector & AI Data Systems:

• Lead the design and management of vector database ecosystems to support RAG and LLM-driven applications.

• Define strategies for embedding pipelines, chunking, indexing, and hybrid search (vector + keyword/metadata).

• Optimize retrieval quality, latency, and relevance for AI-driven sales insights.

Data Engineering & Storage Architecture:

• Architect and oversee data pipelines that ingest, transform, and synchronize data across blob storage, warehouses, and vector stores.

• Establish patterns for multi-modal data handling (text, PDFs, structured data, CRM records).

• Ensure interoperability between enterprise data platforms (e.g., Snowflake, Databricks) and AI-specific storage systems.

API & Data Access Layer:

• Define and implement GraphQL and REST API strategies to expose data services for AI applications like Sales Assist.

• Build and govern secure, scalable API layers that support real-time inference and retrieval workflows.

• Standardize schema design, versioning, and access control for internal and external consumers.

Team Leadership & Delivery:

• Lead and manage a team of data engineers, platform engineers, and data architects responsible for building and maintaining these systems.

• Set technical direction, best practices, and coding standards for AI data infrastructure.

• Drive execution through agile delivery, ensuring timelines, quality, and reliability standards are met.

• Mentor team members and build a high-performing AI data engineering capability within the organization.

Governance, Reliability & Compliance:

• Implement data governance, security, and compliance controls across all data platforms.

• Ensure systems meet regulatory requirements (e.g., SEC, GDPR, CCPA) and internal risk standards.

• Establish monitoring, observability, and incident response practices for data pipelines and storage systems.

What Will Help You Be Successful in This Role?

Required:

• 10+ years of experience in data engineering, data platform architecture, or AI infrastructure roles.

• Proven experience building and operating production-grade data systems, including:

  • Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus)

  • Object/blob storage (e.g., S3, Azure Blob)

  • Relational databases (e.g., PostgreSQL, SQL Server)

• Strong expertise in API design and implementation (GraphQL and/or REST).

• Hands-on experience with data pipelines, distributed systems, and cloud platforms (AWS, Azure, or GCP).

• Demonstrated experience leading and managing engineering teams.

• Strong understanding of performance optimization, scalability, and reliability engineering.

Preferred:

• Experience with LLM applications, RAG architectures, and semantic search systems.

• Background in financial services or asset management, particularly sales or client data domains.

• Familiarity with MLOps, feature stores, and AI platform tooling.

• Experience with data observability tools and modern orchestration frameworks (e.g., Airflow, Dagster).

Success in This Role:

• Deliver a robust, scalable, and low-latency data platform that powers AI applications in production.

• Enable high-quality retrieval and data access for Sales Assist and future AI initiatives.

• Build and lead a strong engineering team capable of sustaining and evolving AI data infrasatructure.

Job Level - Individual Contributor

Work Shift Timings - 2:00 PM - 11:00 PM IST