Develop and implement advanced machine learning models and algorithms for prediction, analysis, and optimization of concrete performance
Optimize models for improved accuracy, robustness, and performance
Build and maintain agentic AI systems capable of multi-step reasoning, tool usage, and workflow orchestration
Utilize strong knowledge of machine learning frameworks such as Keras, TensorFlow, or similar tools to develop and fine-tune models
Stay up to date with the latest advancements in data science, machine learning, large language models, and agentic AI
Apply expertise in Python and SQL to manipulate, analyze, and transform large datasets
Develop retrieval-augmented generation (RAG) pipelines by integrating structured and unstructured data sources with LLMs
Develop, and deploy LLM-based solutions to support intelligent reasoning, automation, and decision-making within products and internal tools
Solve complex problems using data-driven and AI-powered approaches

Requirements

Master’s or PhD in Data Science, Computer Science, Statistics, Mathematics, or a related field
Proficiency in programming languages such as Python
Proven experience leveraging machine learning frameworks such as Keras, TensorFlow, or similar tools
Strong understanding of prompt engineering, LLM evaluation techniques, and reliability considerations
Familiarity with retrieval-augmented generation (RAG) architectures and vector search technologies
Enthusiasm for learning new technologies and sharing knowledge with others
Demonstrated experience analyzing, modeling, training, and deploying machine learning and AI solutions into production
Proficiency with data science libraries such as pandas, NumPy, and Matplotlib
Experience building agent-based or agentic AI systems using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent
Hands-on experience working with Large Language Models (LLMs) (e.g., OpenAI, Azure OpenAI, Hugging Face, or similar platforms)
Hands-on experience with databases such as PostgreSQL and MongoDB, including efficient data querying and manipulation
Strong experience applying supervised and unsupervised machine learning techniques to build predictive and prescriptive solutions

Benefits

Hybrid work week
Competitive company-paid benefits plan
Generous professional development budget
Volunteer and Life Days that make work/life balance easier to achieve
Your birthday off (because we want you to celebrate)
RRSP matching program through RBC

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningPythonSQLKerasTensorFlowdata analysisprompt engineeringretrieval-augmented generationsupervised machine learningunsupervised machine learning

Soft Skills

problem solvingenthusiasm for learningknowledge sharing

Certifications

Master’s in Data SciencePhD in Data ScienceMaster’s in Computer SciencePhD in Computer ScienceMaster’s in StatisticsPhD in StatisticsMaster’s in MathematicsPhD in Mathematics