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Hyderabad, Telangana, India
Onsite
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.
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.
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
At Franklin Templeton, we believe your benefits should support your life, your goals, and your future. That’s why we offer a comprehensive Total Rewards package designed to help you thrive both personally and professionally.
Highlights of our benefits include:
Franklin Templeton is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all applicants and existing employees, and we evaluate qualified applicants without regard to ancestry, age, color, disability, genetic information, gender, gender identity, or gender expression, marital status, medical condition, military or veteran status, national origin, race, religion, sex, sexual orientation, and any other basis protected by federal, state, or local law, ordinance, or regulation.
