Principal Cloud Security Engineer

Application Deadline:

10/29/2026

Address:

320 S Canal Street

Job Family Group:

Technology

We are seeking an enthusiastic and passionate professional for a Senior Cloud, AI & Data Security Engineer role who wants to design and implement security solutions for systems and services across AWS, Azure, and AI/ML platforms . We need someone who can establish the highest standards that meet and exceed security governance solutions and practices, provide assurance to management and auditors, and ensure sustained protection by embedding controls in operational and DevOps (CI/CD) practices with a focus on automation.

We are looking for someone who has a high level of technical security expertise and who takes seriously the responsibility of monitoring, detecting, protecting, and maintaining the security of data, AI/ML systems, cloud platforms, and networks .

You are a leader with a strong technical background. You have demonstrated strength in:

Developing and implementing secure cloud and AI/ML architectures using a risk-based cybersecurity and data privacy strategy

Defining security patterns, roadmaps, and operating models that leverage collaboration

Facilitating industry-standard information security governance

Advising senior leadership on cybersecurity, AI risk , and privacy risks, threats, and investment strategies

Documenting appropriate policies and procedures to manage information security risks, including those unique to AI/ML systems and sensitive data assets

As a qualified candidate, you will be part of the team driving BMO's Cloud, AI, and Data Security implementation. As a member of this team, you should possess the ability to inspire yourself and all of our team. Based on your previous experiences, you will inject new knowledge and skills into an already high-performing team, thus elevating our efforts to new heights.

Your Responsibilities

Cloud Security

Assess, design, implement, automate, and document security solutions, controls, and processes for Amazon Web Services (AWS) and Microsoft Azure cloud platforms

Develop and maintain security patterns for cloud platforms and services; assess all cloud patterns to ensure adherence to best security practices and controls

Design and implement security baseline controls for Cloud Services for integration into the CI/CD process

Build and deliver policies as code , automating security controls and best practices

Review and approve code and changes with security implications (e.g., IAM Roles and Policies, Security Groups, etc.)

Be the cloud security subject matter expert for the Cloud Engineering group and its partners in any IaaS, PaaS, and SaaS implementations

AI & Machine Learning Security

Define and implement a security framework for AI/ML systems , covering the full model lifecycle from data ingestion and training to deployment and monitoring

Assess and mitigate AI-specific threats including adversarial attacks, model inversion, data poisoning, prompt injection, and model theft

Evaluate and secure AI/ML platforms and tools (e.g., Amazon SageMaker, Azure Machine Learning, Hugging Face, OpenAI APIs) against organizational risk standards

Collaborate with data science and AI engineering teams to integrate security controls into MLOps pipelines , ensuring model integrity, access controls, and auditability

Monitor emerging AI threat landscapes and regulatory developments (e.g., EU AI Act, NIST AI RMF) and translate these into actionable organizational controls

Data Security

Implement and manage data security posture management (DSPM) tools to continuously monitor sensitive data exposure across cloud environments

Establish controls for structured and unstructured data stores , including databases, data lakes, data warehouses (e.g., Snowflake, AWS S3, Azure Data Lake), and file sharing platforms

Drive the adoption of data-centric security practices within application development and analytics teams

General Security Leadership

Provide subject matter expertise on architecture, authentication, and systems security based on a clear understanding of the engineering stack, services, and data flow

Lead focused and continuous cybersecurity risk assessments of new and existing technologies - including AI/ML systems and data platforms - to identify risks and appropriate controls that balance security and operability

Provide effective and pragmatic cybersecurity guidance upfront in major technology projects to enable the business to innovate securely

Assist in the investigation and remediation of security incidents and issues, including those involving AI model compromise or data breaches

Work closely with Information Security, product, and software development teams to assess cybersecurity risk and recommend solutions in cloud, AI, and data environments

Your Mindset

You are a self-starter , driven, and can handle multiple projects and priorities

You are passionate about driving the DevSecOps and MLSecOps mindset and culture in a fast-paced, challenging environment where you get the opportunity to work with the latest tools and technologies

You understand the intersection of security, AI, and data , and actively seek to build bridges between these disciplines

You are actively looking to improve the solutions you implement, understand the efficacy of collaboration, and are keen to work in a team of CI/CD, infrastructure, AI, and data specialists

You are energized by the rapidly evolving AI threat landscape and bring intellectual curiosity and practical judgment to navigating ambiguity

As a member of this team, you will inject new knowledge and skills into an already high-performing team, elevating our collective efforts to new heights

Required Core Skills

Foundational

A university degree in Engineering, Computer Science, Information Technology , or a related field

7-10 years of experience developing and implementing security architectures and/or engineering, with demonstrated breadth across cloud, data, and/or AI security domains

Security certifications such as CISSP, CCSP, CCSK , or any Cloud Security Specialty certification (e.g., AWS Certified Security Specialty, Microsoft Certified: Azure Security Engineer Associate)

Emerging/preferred: Certifications or demonstrated knowledge in AI security (e.g., CDAI, CompTIA AI+, or equivalent vendor-specific AI security training) or data security (e.g., CDPSE, CIPP)

Cloud Security

Demonstrated knowledge of cloud architecture, cloud operations, cloud-based identity and access management, security automation, and orchestration

Extensive experience with cloud-native security solutions and tools (e.g., AWS Security Hub, AWS GuardDuty, Microsoft Defender for Cloud, Azure Sentinel)

Knowledge of technical security control environments and compliance frameworks including CSA CCM, ISO 27001, ISO 27017, and NIST CSF

AI & ML Security

Working knowledge of AI/ML development frameworks and platforms (e.g., TensorFlow, PyTorch, SageMaker, Azure ML) and associated security risks

Familiarity with the OWASP Top 10 for LLMs , MITRE ATLAS , and NIST AI Risk Management Framework (AI RMF)

Understanding of MLOps pipeline security , including securing model registries, feature stores, training environments, and inference endpoints

Knowledge of Generative AI security risks , including prompt injection, jailbreaking, data leakage via LLMs, and supply chain risks in AI model dependencies

Data Security

Experience implementing data loss prevention (DLP) , data classification , and data access governance solutions in enterprise environments

Knowledge of DSPM tools and practices

Understanding of data encryption at rest and in transit , tokenization, and key management for large-scale data environments

Familiarity with data privacy regulations (e.g., PIPEDA, GDPR, CCPA) and their technical implementation requirements

Experience securing cloud-based