AI Engineer (Global Security)
As an AI Engineer (Global Security), you will contribute to building RBC’s next-generation platform for autonomous and semi-autonomous AI. You will contribute to the engineering lifecycle to create self-directed, context-aware AI agents and workflows that operate with minimal human intervention, leveraging your expertise in large language models (LLMs), generative AI, machine learning, and data pipelines. You’ll work to deliver highly scalable, production-grade AI solutions that automate complex controls, accelerate regulatory readiness, and set new standards for operational excellence and security.
What will you do?
- Develop and deploy enterprise AI solutions using Python to automate threat detection and remediation processes at scale
- Evaluate and recommend technical approaches (multi-agent, single-agent, rule-based) based on threat complexity and regulatory requirements, bringing strategic thinking to architecture decisions
- Contribute to the development of LLMs and RAG systems including output quality benchmarking and model performance monitoring (OpenAI, Cohere, Claude via RBC's Claude shop), vector search (pgvector, Milvus, Pinecone), and context engineering pipelines to ensure AI threat detection is accurate, secure, and aligned with data
- Build and maintain data pipelines and context layers (Spark, Airflow, SQL/NoSQL) to deliver high-quality intelligence for AI-driven threat detection and automation
- Integrate AI solutions with enterprise systems and platforms (Docker, GitHub Actions) and production monitoring tools
- Follow and apply best practices in AI safety, privacy, regulatory compliance, and autonomous system guardrails, including model monitoring, fallback mechanisms, and secure deployment in regulated environments (NIST 800-53, SOX)
- Collaborate with Global Security, Data Science, and cross-functional teams to refine threat detection requirements, share knowledge, and maintain high-quality technical documentation
What do you need to succeed?
- Must-have
- Bachelor's degree (or equivalent) in Computer Science, Software Engineering, or a related field
- 2-5 years of software engineering experience with Python and at least one of Java, TypeScript, or Go
- 2+ years of hands-on experience with machine learning, data science, or AI (demonstrated ability to learn and apply generative AI approaches)
- Hands-on experience deploying LLMs, RAG systems, and agent orchestration frameworks (e.g., LangChain, CrewAI, AutoGen), including vector database configuration and context engineering for AI workflows
- Proficient with MLOps/DevOps, containers, Kubernetes, CI/CD pipelines (Docker, GitHub Actions, Jenkins)
- Nice-to-have
- Experience with threat detection, security analytics, or threat monitoring systems
- Strong mathematical foundations in probability, statistics, and optimization for model design, along with familiarity with distributed ML frameworks (Ray, SageMaker) and real-time pipelines (Kafka, Kinesis)
- Awareness of regulatory frameworks and compliance standards
