What is the opportunity?
We’re looking for a Lead AI/ML Software Engineer to drive innovation at the intersection of AI / Machine Learning and financial services. You’ll be responsible for owning and delivering projects end to end – from design, development, testing and release of the product to production. In addition to software development tasks, the role will also include machine learning tasks such as data pre-processing and exploration, building and scaling ML algorithms and pipelines and deployment and monitoring of production systems. At RBC Digital, you’ll be joining a team that works on world class digital experiences and has access to rich and massive datasets and offers the computational resources to support cutting‑edge machine learning development.

Responsibilities

• Design, develop, and maintain scalable backend components and ML models in support of Online Banking and digital channel applications.


• Participate in code reviews, testing, and documentation to ensure best practices and code quality.


• Apply software engineering and ML best practices to architect robust, scalable machine learning systems.


• Troubleshoot and resolve technical issues in a timely manner.


• Work in an agile environment, actively participating in sprint planning, stand‑ups, and retrospectives.


• Strong written and verbal communication skills, with the ability to work cross‑functionally and influence stakeholders.

Qualifications

• PhD or Master’s degree in Computer Science, Machine Learning, or equivalent hands‑on experience.


• Five or more years building and deploying Machine Learning models in production for real business problems.


• Advanced proficiency in Java/Python, with demonstrated ability to write production‑grade code and documentation.


• Advanced proficiency with large language model architectures, including inference (TensorFlow or PyTorch), fine‑tuning, and model deployment.






• Strong understanding and work experience with retrieval‑augmented generation (RAG) systems, agentic systems, orchestration frameworks, context and memory management, and tool/skills integration patterns.


• In‑depth knowledge of embeddings, re‑rankers, and vector databases.


• Expertise in ML experimentation, model evaluation, and production observability.


• Proven experience building and deploying RESTful APIs for ML model serving (FastAPI, Flask).


• Demonstrated proficiency with MLOps practices, including model serving, monitoring, CI/CD pipelines for ML systems, and containerized workload deployment (OpenShift Container Platform / OCP4 or Kubernetes).


• Proficiency with AI‑assisted software development tools (e.g., GitHub Copilot, Windsurf AI, Anthropic Claude Code).


• Exceptional verbal and written communication skills with proven ability to collaborate effectively across cross‑functional teams (business, engineering, model risk management).

Nice to Have

• Experience with Small Language Models (SLMs) for on‑device inference, domain specialization through fine‑tuning, or Reinforcement Learning applications.


• Track record of adopting and leveraging AI‑driven solutions to enhance productivity, automate routine tasks, and drive efficiency.


• Demonstrates curiosity about emerging AI capabilities and a thoughtful approach to applying them for enhanced client outcomes.


• Experience in managing, mentoring, or coaching team members and empowering junior engineers to grow and succeed.

Benefits

• A comprehensive Total Rewards Program including bonuses and flexible benefits.


• Leaders who support your development through coaching and managing opportunities.


• Ability to make a difference and lasting impact.


• Work in a dynamic, team-oriented, progressive, and high‑performing team.


• Access to a variety of job opportunities across business and geographies.