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AI Engineering Lead

Job in Toronto, Ontario, C6A, Canada
Listing for: Agilus Work Solutions
Full Time position
Listed on 2026-08-28
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 120000 - 180000 CAD Yearly CAD 120000.00 180000.00 YEAR
Job Description & How to Apply Below

We are seeking a forward-thinking AI Engineering Lead to join our clients innovative team and drive the future of Property & Casualty (P&C) insurance. As an AI Engineering Lead, you will serve as a technical authority and strategic leader in developing and deploying sophisticated AI-powered solutions to drive business value and innovation across the P&C insurance value chain.

Hybrid

2-3 days on site in Toronto.

  • Define and execute the long-term technical strategy for AI-driven business solutions.
  • Design and Implement AI solutions using machine learning models (including LLMs, graph neural networks, and computer vision)
  • Experience in Fraud or working on a Fraud Detection Platform
  • Lead and mentor other AI engineers and data scientists.
  • Collaborate with senior leadership to align AI initiatives with business goals.
  • Remain at the forefront of advancements in AI and data engineering.
Requirements
  • Mastery of Python and deep experience with AI/ML frameworks, ability to write clean, high-performance code in Python, Java, SQL, and Spark.
  • Deep expertise in a wide range of AI/ML techniques, building and deploying AI based applications, RAG pipelines, and agent-based workflows.
  • Understanding of complex data structures, advanced data modeling, and the management of both structured and unstructured data at scale.
  • Extensive hands-on experience architecting and deploying AI solutions on GCP or AWS - Vertex AI and Gemini, is a significant asset.
  • Experience designing and working with A2A, MCP, Agentic RAG, and including conventional microservices, and event-drive architecture.
  • Expertise in DEV/MLOps, including advanced containerization with Docker and Kubernetes, CI/CD for machine learning, Data, and AI observability.
  • Authoritative knowledge of the principles and best practices for building ethical, fair, and transparent AI systems.
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