Gen AI Data Engineer

Expedite Technology Solutions; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

A leading technology firm in Toronto seeks a skilled backend developer to design and build GenAI-driven systems. The ideal candidate must have strong Python skills and experience with Kubernetes and Docker. Responsibilities include developing scalable applications and optimizing high-throughput environments. Familiarity with cloud platforms, particularly Azure, is essential for this role.

Qualifications

• Strong backend development skills required.
• Experience with API deployment at scale is necessary.
• Deep understanding of data workflows is essential.

Responsibilities

• Design and build GenAI-driven systems.
• Debugging and optimization in high-throughput environments expected.
• Work with cloud platforms like Azure.

Skills

Backend development skills in Python

Experience with FastAPI

Solid with Kubernetes

Proficient in Docker

API deployment skills

Understanding of Databricks

Knowledge of PySpark

Cloud platforms experience, especially Azure

Tools

MLflow

Airflow

Snowflake

Job description

Job Description

Must Have

• Design, build, and scale GenAI-driven systems that power research digitization, banking workflows, global markets, and monetization pipelines
• Strong backend development skills in Python, FastAPI, and async programming
• Solid hands‑on experience with Kubernetes, Docker, and API deployment at scale
• Deep understanding of Databricks, *** Lake, PySpark, and distributed data workflows
• Proven experience building or integrating with LLM‑based applications, including prompt routing or semantic matching
• Client is doing significant checks on Random Forest and Decision Tree capabilities
• Excellent debugging, profiling, and optimization skills in high‑throughput environments
• Comfort working with cloud platforms, especially Azure

Nice to Have

• Familiarity with model orchestration frameworks (LangChain, LlamaIndex, or similar)
• Experience designing or contributing to MCP‑style architectures (multi‑modal, intent‑aware, tool‑executing systems)
• Working knowledge of MLflow, Airflow, or Snowflake
• Exposure to alternative data sources (web, satellite, social, geospatial) and their AI use cases
• Understanding of enterprise CI/CD, secrets management, and secure API gateways