Principal AI Data Strategy Consultant

Job Description :

A leading asset management company is building next-generation AI capabilities to power Sales Assista 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 :

i. Vector databases for embeddings and semantic retrieval

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

iii. Relational databases (SQL) for structured and transactional data

iv. 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 :

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

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

iii. 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 infrastructure.

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