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Data Engineer

2 weeks ago

Toronto, Ontario, Canada Citrine Solution Full time

We are seeking a visionary
Principal Data Engineer
to serve as a technical cornerstone for our AI and Data organization. In this high-impact leadership role, you will be responsible for designing and overseeing the architecture of our enterprise data management systems.

The ideal candidate is a "hands-on architect" with a deep mastery of modern data infrastructure and specialized experience in
Generative AI (GenAI)
and
Agentic AI frameworks
. You will not only build scalable systems but also provide strategic direction, mentorship, and technical roadmaps for our engineering teams.

Key Responsibilities

  • Architectural Leadership:
    Design, build, and maintain robust, scalable data architectures that support high-volume data processing and real-time AI applications.
  • AI Solution Design:
    Architect data pipelines specifically optimized for
    Large Language Models (LLMs)
    , including RAG (Retrieval-Augmented Generation) patterns and Agentic workflows.
  • Infrastructure Integrity:
    Ensure the performance, security, and scalability of data management systems across cloud and hybrid environments.
  • Cross-Functional Collaboration:
    Make data seamlessly accessible to Data Scientists, Analysts, and Executive stakeholders to drive informed decision-making.
  • Team Direction:
    Provide technical mentorship to senior engineers, conduct deep-dive architectural reviews, and establish engineering best practices.
  • Agentic AI Implementation:
    Build the data backbone required for autonomous AI agents, focusing on state management, memory persistence, and tool-calling capabilities.

Required Qualifications

  • Experience:
    10+ years in Data Engineering, with at least 3+ years in a Lead or Architect capacity.
  • AI Specialization:
    Proven experience implementing
    GenAI
    solutions and a strong understanding of
    Agentic AI
    frameworks (e.g., LangChain, AutoGPT, CrewAI, or similar).
  • Data Engineering Stack:
    Mastery of Python, Scala, or Java; expert-level SQL; and experience with modern data stack tools (e.g., Spark, Flink, Airflow, dbt).
  • Cloud & Vector DBs:
    Deep expertise in cloud data services (AWS/Azure/GCP) and experience with Vector Databases (e.g., Pinecone, Weaviate, Milvus).
  • Database Expertise:
    Proficiency in both RDBMS and NoSQL systems, with a focus on high-performance indexing and query optimization.
  • Education:
    Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.

Preferred Skills

  • Experience with
    LLMops
    and deploying AI models into production at scale.
  • Strong understanding of data governance, privacy, and security in the context of AI.
  • Excellent communication skills with the ability to translate complex technical concepts into business strategy.