Student machine learning software engineer building end - to - end AI solutions at RBC Borealis, Royal Bank of Canada’s AI and data innovation group. Collaborating with researchers and business teams on algorithms, distributed data processing, and production - ready software.
About the role
- Senior AI/ML Engineer designing and deploying ML solutions for real-time video analytics. Collaborating across engineering, research, and software engineering to create scalable, reliable features.
Responsibilities
- Design, develop, train, and deploy ML models—including computer vision, LLMs/VLMs, and multimodal models—across cloud and edge/embedded environments.
- Own ML-driven features end-to-end: from proof of concept and experimentation to integration, deployment, instrumentation, and ongoing improvement.
- Evaluate and integrate third-party AI/LLM/VLM services, balancing cost, performance, and applicability for customer-facing use cases.
- Build and maintain MLOps pipelines supporting data preparation, experimentation, training, evaluation, model versioning, serving, monitoring, and continuous improvement.
- Collaborate with engineering and product teams to define model requirements and translate customer problems into valuable predictive insight features.
- Implement best practices for quality, observability, alerting, and performance tuning related to AI services and ML model behavior.
- Integrate ML components with Solink’s broader platform, including backend services and edge processing systems.
- Develop automated tests for ML and software components to ensure stable, reliable releases.
- Troubleshoot and resolve production AI/ML issues while driving long-term architectural improvements.
- Stay current with advancements in computer vision, generative AI, LLM/VLM architectures, and applied machine learning.
Requirements
- 7+ years of experience building and deploying production-grade software and integrating ML models into real products.
- Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
- Hands-on experience training, fine-tuning, evaluating, and optimizing ML models (including model distillation or compression).
- Experience implementing MLOps practices (data pipelines, experimentation, serving, monitoring, retraining).
- Demonstrated ability to evaluate third-party LLM/VLM and AI services and make informed recommendations.
- Experience building solutions for cloud environments (AWS preferred) or for edge/embedded systems (Ubuntu, AI accelerators, device hardware), with knowledge of performance trade-offs.
- Strong software engineering skills, capable of delivering scalable, production-ready ML systems.
- Excellent communication skills and ability to explain ML concepts clearly to technical and non-technical audiences.
Benefits
- Flexible work where possible, with clear expectations and trust.
- Meaningful equity for all full-time permanent employees.
- Comprehensive benefits, including fully paid health & dental (no waiting period) + $500 HSA.
- Wellness support with monthly reimbursement for fitness, wellness, or mental health.
- Growth through merit—advancement is based on contribution and impact.
- Transparent, candid culture with clear expectations and honest feedback.
- Connection & community through So-learns, Solink-o, team events, and more.
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