Technical VP - AI Data Platform

Huawei Technologies Canada Co., Ltd.; Markham; On-site; CAD 180,000 - 230,000; Full time

Job summary

Huawei Canada invites a Technical VP to lead the Emerging Storage Lab, directing AI data platform strategy from data lakes to vector indexes. You will guide architecture, performance optimization, and end‑to‑end data pipelines, while mentoring leading engineers and researchers in IR, AI models, and retrieval systems.

The role emphasizes publication, patenting, and industry representation to advance enterprise AI data management at scale.

Qualifications

• 5+ years in data systems research/engineering with focus on AI data platforms.
• 2+ years in a technical leadership role overseeing data infrastructure for AI/ML workloads.
• Proven track record in data management, unstructured data processing or AI scalability.
• Experience with Data Lake platforms (Databricks, Snowflake) and IR/vector tech (Milvus, Pinecone, Cohere).
• Deep fluency in AI data platform challenges: hybrid search, vector indices, latency vs. cost.

Responsibilities

• Define the tech roadmap for next‑gen data storage and AI retrieval systems.
• Lead lab transition to Data‑Centric AI, guiding architecture and performance optimization.
• Prototype AI data stack architectures unifying file storage, data lakes and vector indexes.
• Mentor engineers/researchers in IR, AI models, and retrieval mechanisms.
• Engage with industry forums to shape standards and represent Huawei Canada.

Skills

Data systems research

Data infrastructure for AI/ML

Technical leadership

Hybrid search tuning

Index freshness

Vector space multi-tenancy

Executive communication

Mentoring leaders

LLM context engineering

Tools

Databricks

Snowflake

Milvus

Pinecone

Cohere

Job description

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

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.