Cyber AI and Data Engineering Senior Associate

Pwc Canada•Toronto, ON

5d•Hybrid

About The Position

At PwC, our people in cybersecurity focus on protecting organisations from cyber threats through advanced technologies and strategies. They work to identify vulnerabilities, develop secure systems, and provide proactive solutions to safeguard sensitive data. As a cybersecurity generalist at PwC, you will focus on providing comprehensive security solutions and experience across various domains, maintaining the protection of client systems and data. You will apply a broad understanding of cybersecurity principles and practices to address diverse security challenges effectively. The Opportunity: As a Cyber AI and Data Engineering Senior Associate , unlock your potential and embrace the chance to drive meaningful outcomes that’ll elevate your career. Your role will include, but isn’t limited to: Support building agents in Databricks, Co-Pilot or other LLM tools. Develop MCP servers to connect to core cyber and data infrastructure. Act as a primary interface between security teams and AI capabilities. Help improve our AI frameworks with increased observability, updated governance policies, etc.

Requirements

2+ years working in cybersecurity engineering or DevSecOps role
1+ year working with LLMs, and AI application development (prompt engineering, RAG, etc)
Experience with MCP and orchestration frameworks (LangChain / LangGraph) for LLM agents
Proficiency in python, REST APIs (OpenAI API etc)
Familiarity with security platforms such as Microsoft Defender, Google SecOps, CrowdStrike or similar
Familiarity with cloud environments such as Azure or AWS
Enhanced Security Clearance is mandatory.

Responsibilities

Agent Development
MCP Tool Development and Integration: Design, build, and maintain a library of MCP-compliant tools and servers that integrate across the security stack — SIEM, EDR/XDR, SOAR, threat intelligence platforms, and ticketing systems
Team Enablement: Build intuitive AI-powered interfaces, copilots, and chat assistants that allow security users to run investigations and query data in natural language
Reporting and Documentation: Maintain a centralized, version-controlled knowledge base covering all MCP tools, agent configurations, and workflow logic, alongside a living AI risk register that documents known limitations, failure modes, and compensating controls for every deployed system
Collaboration and Communication: Work closely with other cybersecurity, infrastructure, and data teams.
Collaborate with team members and participate in knowledge sharing on AI agents and agentic building best practices.
Continuous Improvement: Continuously evaluate and improve AI methodologies and tools.
Assist with quality checks, lessons learned activities, and root cause analysis across supported environments.
Professional Development: Develop practical expertise in building agentic capabilities and leveraging industry best practices.
Pursue continuous professional development to strengthen knowledge of emerging tools and technologies.