Senior AI Engineer

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Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate's relevant knowledge, skills, and experience.

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

We are seeking an experienced Senior AI Engineer to design, develop, deploy, and scale AI-powered applications across the enterprise. The ideal candidate combines strong software engineering expertise with deep knowledge of machine learning, Generative AI, agentic systems, cloud platforms, and MLOps practices.
You will work closely with data scientists, platform engineers, architects, product teams, and business stakeholders to bring AI solutions from concept to production while ensuring security, governance, scalability, and regulatory compliance.

AI Solution Development

• Design and implement AI/ML and Generative AI solutions for business-critical use cases.
• Build intelligent applications leveraging LLMs, RAG architectures, vector databases, and agentic workflows.
• Develop APIs, microservices, and reusable AI services for enterprise consumption.
• Implement prompt engineering, tool calling, function orchestration, and agent frameworks.

Enterprise AI Platform Engineering

• Contribute to the design and evolution of Scotiabank's AI platform.
• Build scalable AI services on cloud-native architectures.
• Enable secure deployment of foundation models and AI agents.
• Integrate AI capabilities with enterprise data platforms and business applications.

MLOps & AI Operations

• Develop CI/CD pipelines for machine learning and Generative AI workloads.
• Automate model deployment, monitoring, evaluation, and lifecycle management.
• Implement observability, telemetry, model performance tracking, and cost governance.
• Ensure AI solutions meet enterprise reliability and security standards.

Data & Knowledge Engineering

• Build RAG pipelines using structured and unstructured data sources.
• Develop data ingestion, embedding, indexing, and retrieval frameworks.
• Work with large-scale data platforms including batch and real-time processing systems.
• Collaborate with data governance teams to ensure responsible AI practices.

AI Governance & Risk Management

• Implement AI governance controls, guardrails, content filtering, and responsible AI requirements.
• Support regulatory and risk-management processes for AI systems.
• Conduct model validations, performance assessments, explainability, and compliance reviews.

Stakeholder Collaboration

• Partner with business leaders to identify impactful AI opportunities.
• Mentor junior engineers and promote engineering best practices.
• Collaborate with architecture, security, and infrastructure teams on enterprise initiatives.

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field.

Experience

• 5+ years of software engineering experience.
• 3+ years of AI/ML engineering experience in production environments.
• Experience deploying enterprise-scale AI solutions.
• Experience working in highly regulated environments is preferred.

Programming

• Python (required)
• Java or Scala
• SQL
• REST APIs

AI & Machine Learning

• Machine Learning model development and deployment
• Deep Learning frameworks (PyTorch, Tensor Flow)
• NLP and LLM technologies
• Fine-tuning and model optimization
• Evaluation frameworks

Generative AI

• OpenAI / Azure OpenAI
• Lang Chain, Lang Graph, Semantic Kernel
• RAG architectures
• Vector databases
• Agentic AI frameworks
• Prompt engineering

Cloud & Dev Ops

• Microsoft Azure (preferred)
• AWS or GCP experience
• Docker
• Kubernetes
• Terraform
• Git Hub Actions / Azure Dev Ops

Data Technologies

• Databricks
• Spark
• Kafka
• Da…