Technical VP - AI Data Platform

huaweicanada; Markham; On-site; CAD 220,000 - 320,000; Full time

Job summary

Huawei Canada is seeking a Technical VP to define the technology roadmap for next-generation data storage and AI retrieval systems. You will lead the Emerging Storage Lab at the Toronto Research Centre, shaping the AI data stack, from unstructured data ingestion to vector indexes, and drive innovation in RAG-optimized data intelligence.

The role combines architecture leadership with hands-on work, mentoring engineers and researchers across IR, AI models, and data lakes, and contributing to

Qualifications

• 5+ years of work experience in data systems research/engineering, with 2+ years in a technical leadership role.
• Proven track record of pioneering work in data management or AI/ML scalability.
• Hands-on experience with data storage architectures and AI infrastructure.
• Fluent communication of complex data concepts to executives and engineers.

Responsibilities

• Define the technology roadmap for next-generation data storage and AI retrieval systems.
• Lead the lab’s transition to Data-Centric AI and RAG-optimized data intelligence systems.
• Architect and evangelize AI Data Platform architectures end-to-end.
• Mentor engineers and researchers in IR, AI Models, and data lakes.
• Publish influential research and represent Huawei Canada at top-tier forums.

Skills

Data systems leadership

AI/ML infrastructure

Data storage architectures

Hybrid search tuning

Index freshness

Vector-space multi-tenancy

Communication skills

Mentoring leaders

Research publishing

Tools

Databricks

Snowflake

Milvus

Pinecone

Cohere

Job description

Huawei Canada has an immediate permanent opening for a Technical VP.

About the team:

The Emerging Storage Lab is a research group based at Huawei Canada's Toronto Research Centre that is focused on next-generation data and storage technologies and innovations. Our team comprises graduate computer engineers and computer scientists with diverse industry experience, ranging from 0 to over 20 years. This lab investigates various data storage-related topics, including data management, data catalog, data fabric, file systems, storage networks, and AI storage, aiming to advance the field and drive data storage technological progress in the new AI era.

About the job:

• Define the technology roadmap for next-generation data storage and AI retrieval systems, aligning with global R&D and business objectives. Set the global research agenda for AI Data Platforms, with a specific focus on vector-native data lakes, intelligent caching layers, and high-performance retrieval infrastructures that power Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
• Lead the lab’s transition to Data-Centric AI, pioneering research in Agent memory system, Knowledge Base, and RAG-optimized data intelligence systems. Drive innovation in dynamic data indexing, hybrid search (semantic + keyword), chunking/parsing strategies, and real-time context freshness—ensuring our storage architectures evolve to handle the unique throughput, latency and accuracy demands at petabyte scale.
• Serve as both Architect and Evangelist, shaping the technical roadmap while maintaining hands‑on involvement in critical projects. Lead architecture reviews and performance optimizations for end-to-end AI Data Platform, from unstructured data ingestion and metadata enrichment to vector database sharding and reranking strategies. Prototype next‑gen architectures that unify file storage, data lakes, and low‑latency vector indexes into a cohesive, AI‑ready data stack.
• Lead a world‑class research lab, mentoring top‑tier engineers and researchers in the specialized fields of Information Retrieval (IR), AI Model, Data Lake, and retrieval mechanisms. Foster a culture of innovation and collaboration focused on solving the challenges for enterprise AI.
• Shape industry standards by publishing influential research on Agent Memory, Knowledge Base, RAG, patenting novel approaches, and representing the company in top‑tier tech forums (e.g., VLDB, SIGIR, NeurIPS) to define the future of AI‑native data management.

About the ideal candidate:

• 5+ years’ work experience in data systems research/engineering, with 2+ years in a technical leadership role, specifically focused on data infrastructure for AI/ML workloads.
• Proven track record of delivering industry‑leading research and pioneering work in data management, unstructured data processing, scalable storage systems, or AI/ML scalability—with demonstrable experience in Data Lake (e.g., Databricks, Snowflake), information retrieval systems (e.g., Milvus, Pinecone, Cohere), or LLM context engineering.
• Extensive hands‑on experience and deep expertise in data storage architectures (specifically object stores and distributed file systems) OR AI/ML infrastructure.
• Deep technical fluency in the challenges of AI Data Platform: including hybrid search tuning, index freshness, multi‑tenancy in vector spaces, and cost/latency trade‑offs between dense and sparse retrieval methods.
• Exceptional communication skills—able to articulate complex concepts regarding data lifecycle management for Agent and LLMs to executives, and dive into the granular details of technology metrics with engineers.
• Passion for mentoring leaders and fostering innovation in the rapidly evolving intersection of database systems and Generative AI.

Additional Information:

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs.

All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.