AI Engineer II (Experienced)

Location: Toronto (on-site)

Team: Customer Authentication Strategy & Performance (CASP) – Advanced Analytics & AI

About the Role

We are looking for a seasoned AI Engineer II to take ownership of high-impact AI and agentic AI initiatives across enterprise authentication and fraud-prevention domains. You will architect and lead the implementation of large-scale intelligent systems—spanning multi-agent workflows, LLM orchestration, retrieval-augmented generation, and end-to-end automation pipelines—while ensuring compliance with AI governance, risk controls, and enterprise standards.

Key Responsibilities

Lead the architecture, development, and deployment of production-grade AI and agentic AI systems.
Design scalable multi-agent pipelines integrating LLMs, RAG components, graph/vector databases, and microservice infrastructure.
Partner with data science, product, and fraud strategy teams to translate business objectives into AI-driven solutions.
Own observability frameworks for model drift, bias detection, latency, and throughput monitoring.
Build automation and orchestration frameworks that reduce manual workloads and enable adaptive AI responses.
Mentor junior engineers and data scientists on best practices in ML architecture, code quality, and scalable deployment.
Ensure adherence to responsible AI guidelines, security standards, and data privacy requirements.
Lead POCs for emerging AI tech (vendor LLMs, agentic platforms, fine-tuning pipelines) and evaluate enterprise adoption potential.

Qualifications

Master’s or PhD in Computer Science, Engineering, Applied Mathematics, or related field.
5–8 years of AI or ML engineering experience with a proven track record of building and scaling AI systems in production.
Deep expertise in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
Demonstrated experience deploying LLMs, RAG pipelines, and multi-agent orchestration at scale.
Strong understanding of cloud infrastructure (Azure, Databricks, Kubernetes, Docker, MLflow).
Proficiency in data engineering (Spark, SQL), APIs, and microservice architecture.
Excellent system-design skills and ability to evaluate trade-offs between performance, cost, and compliance.
Strong communication and mentoring skills, comfortable presenting technical concepts to executives and cross-functional teams.

Preferred

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.).