Data & AI Architect

1 week ago

Toronto, Ontario, Canada DataPattern Full time

Job Title: Data & AI Architect

Location:
 Toronto.

Work Mode:
 Hybrid

Responsibilities:

Define enterprise data and AI strategies, designing scalable architectures for data warehouses, lakes, and intelligent analytical platforms. Lead modernization initiatives transforming legacy systems into cloud-native, AI-ready solutions. Establish data governance, ML Ops frameworks, and integration patterns for traditional analytics and generative AI applications across diverse client engagements.

You Will:

  • Design end-to-end architectures spanning data platforms, analytics, and AI/ML systems from ingestion through model deployment
  • Develop roadmaps for legacy modernization, cloud migration, and AI adoption including LLM integration strategies
  • Define data and ML modeling standards, feature engineering patterns, and model governance frameworks
  • Architect Gen AI solutions including RAG systems, agent frameworks, and enterprise knowledge bases with proper security controls
  • Evaluate technology stacks for data pipelines, ML platforms, vector databases, and LLM orchestration tools
  • Establish governance frameworks covering data quality, model monitoring, AI ethics, and responsible AI practices
  • Create architectural blueprints, reference architectures, and technical specifications for hybrid data-AI solutions
  • Provide thought leadership on AI/ML trends, emerging technologies, and industry convergence of analytics and intelligence

Qualifications/Requirements:

  • 10+ years in data/analytics with 5+ years architecting solutions spanning traditional BI and AI/ML platforms
  • Deep expertise in data warehousing, data lakes, and modern lake house architectures combined with ML Ops and AI infrastructure
  • Strong knowledge of on-premise and cloud platforms (AWS, Azure, Snowflake, Databricks) plus AI services (Bedrock, Azure Open AI)
  • Experience designing LLM applications, RAG architectures, vector search systems, and agentic AI workflows
  • Understanding of ML lifecycle management, feature stores, model registries, and deployment patterns
  • Proven track record architecting regulatory reporting, financial services, and AI-augmented analytical solutions
  • Knowledge of AI governance, model explainability, bias detection, and responsible AI frameworks
  • Exceptional communicator bridging business strategy, data engineering, and AI innovation for technical and executive audiences

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