AI Engineer

AI Engineer - Authentication

Permanent: $100k-130k / year

Hybrid: 2 days on-site - Downtown Toronto

Job Description:

We are seeking a Senior AI Engineer (Level II) to join the Customer Authentication Strategy & Performance (CASP) – Advanced Analytics & AI team. This role will lead the design and deployment of cutting-edge AI and agentic AI solutions focused on enterprise authentication and fraud prevention. You will architect large-scale intelligent systems, including multi-agent workflows, LLM orchestration, retrieval-augmented generation (RAG), and automation pipelines, while ensuring compliance with AI governance and enterprise standards.

Must-Have Requirements:

• Education: Master’s or PhD in Computer Science, Engineering, Applied Mathematics, or related field.
• Experience: 5–8 years in AI/ML engineering with proven success in building and scaling production-grade AI systems.

Technical Expertise:

• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
• Hands-on experience deploying LLMs, RAG pipelines, and multi-agent orchestration at scale.
• Solid understanding of cloud infrastructure (Azure, Databricks, Kubernetes, Docker, MLflow).
• Proficiency in data engineering (Spark, SQL), APIs, and microservice architecture.
• System Design: Ability to architect scalable solutions and evaluate trade-offs between performance, cost, and compliance.
• Governance & Compliance: Familiarity with responsible AI guidelines, security standards, and data privacy requirements.
• Soft Skills: Excellent communication and mentoring abilities; comfortable presenting technical concepts to executives and cross-functional teams.

Nice to Have:

• Experience in financial services, fraud prevention, identity proofing, or risk analytics.
• Knowledge of AI governance standards and model validation best practices in regulated environments.
• Hands-on experience integrating third-party AI platforms (OpenAI Enterprise, Anthropic Claude, Azure OpenAI, etc.)
• Exposure to observability frameworks for model drift, bias detection, latency, and throughput monitoring.
• Prior experience leading POCs (proof of concepts) for emerging AI technologies and evaluating enterprise adoption potential.