Come and be part of our innovative and high-performing GenAI and Mobile team if you are a talented, tenacious, meticulous, and results-focused individual who thrives on building production-grade GenAI applications. We are seeking an experienced Senior Machine Learning Engineer to help shape, develop, and deliver AI applications and proof-of-concepts (POCs) across diverse business lines. The successful candidate will collaborate with stakeholders to identify opportunities, develop impactful solutions, and drive the adoption of advanced AI technologies across the organization.

Are you a talented, creative, and results-driven professional who thrives on delivering high-performing GenAI applications at scale?

Global Functions

Technologies impact is far-reaching as we collaborate with partners from across the company to deliver innovative and transformational IT solutions. Our clients represent Risk, Finance, HR, CAO, Audit, Legal, Compliance, Financial Crime, Capital Markets, Personal and Commercial Banking and Wealth Management. We also lead the development of digital tools and platforms to enhance collaboration.

As a Senior Machine Learning Engineer, you will be a key member of a team, developing and deploying large-scale GenAI applications for enterprise use cases. You will build and optimize LLM-based solutions, RAG systems, agentic workflows, and production ML infrastructure that powers effective decision-making across RBC. You will be working in a cross-functional team that supports various businesses and you will have an opportunity to work with different kinds of datasets, modern AI frameworks, and cloud-native platforms.

You will collaborate with other developers, ML engineers, and business partners to deliver medium to high-complexity GenAI initiatives with measurable business impact. Develop, and productionize advanced GenAI and AI solutions, ensuring they address complex business challenges with measurable impact and deliver tangible ROI Optimize and deploy state-of-the-art ML models and AI agents, leveraging modern frameworks (e.g., Contribute to experimentation and continuous improvement cycles, including robust prompt engineering, model evaluation, A/B testing,



and performance optimization of production GenAI systems Build and maintain production-grade ML infrastructure including data pipelines, model serving endpoints, monitoring systems, and automated deployment workflows Implement RAG (Retrieval-Augmented Generation) systems using vector databases, semantic search, and knowledge retrieval techniques to enhance LLM capabilities Develop agentic AI workflows with multi-step reasoning, tool use, and orchestration to solve complex business problems autonomously Collaborate with product managers, data engineers, and business stakeholders to translate requirements into technical specifications and working solutions Write clean, maintainable, well-documented code following software engineering best practices including code reviews, testing, and version control Stay current with emerging GenAI technologies and research, evaluating new models, frameworks, and techniques for potential adoption Participate in technical design discussions and contribute to architectural decisions for GenAI applications and ML infrastructure Ensure responsible AI practices including bias detection, model explainability, security, privacy, and compliance with enterprise governance standards Document technical solutions, architectures, and processes to enable knowledge sharing and team scalability 5+ years of experience in machine learning engineering with 2+ years focused on production ML systems ~ Hands-on experience building GenAI applications using LLMs, RAG, agents, or similar technologies in production environments ~ Solid proficiency in Python and modern ML frameworks (LangChain, LangGraph, Hugging Face, OpenAI API, Anthropic Claude, etc.) ~ Solid understanding of LLM architectures, prompt engineering, fine-tuning,



and optimization techniques ~ Experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes) ~ Practical knowledge of MLOps practices including CI/CD, model monitoring, versioning, and deployment automation ~ Experience with vector databases (Pinecone, Weaviate, pgvector, Chroma) and semantic search systems ~ Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent practical experience) Experience in financial services or highly regulated industries with understanding of compliance and data privacy requirements Knowledge of agentic AI architectures and multi-agent orchestration frameworks Experience with real-time streaming data and event-driven architectures Familiarity with distributed systems and high-scale data processing Experience with A/B testing frameworks and experimentation platforms Contributions to open-source ML/AI projects or technical blog posts Experience with graph databases and knowledge graph construction We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

Ability to make a difference and lasting impact on enterprise AI adoption across RBC Access to emerging technologies and continuous learning in the rapidly evolving AI landscape Exposure to diverse business problems across Risk, Finance, Compliance, and other critical functions Big Data Management, Data Mining, Data Science, Deep Learning, Machine Learning (ML), Predictive Analytics, Programming Languages Employment Type: Full time Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all. #

📌 Senior Machine Learning Engineer (Data Science) (Toronto)
🏢 Socket.dev
📍 Toronto