AI Engineer

Full-time, Permanent

Hybrid- 2 days in the downtown Toronto (could potentially go up to 4)

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.

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