Location: Toronto

Duration : 12 months | strong likelihood for an extension, or conversion to full time employee

Daily Responsibilities :

The successful candidate will be focusing on development and testing of AI agents targeting audit efficiency. They may participate in requirements meetings with audit staff, and working closely with the PM/PO. In some cases, they may need to read journal articles to understand the latest developments in the field to leverage state of the art methods and approaches.

• Work closely with stakeholders to understand their needs and build solutions using advanced machine learning methods and Generative AI in the domain of risk and compliance.
• Design and implement end-to-end data pipelines: from data cleaning to model development to writing production level code.
• Implement supervised and unsupervised machine learning models, data mining methods, statistical analysis, and pipelines to prepare and integrate various data types and sources
• Quickly learn new methods, tools and technologies presented in research communities to implement and adapt within our ecosystem and models.
• Collaborate proactively with various business and operation units to design innovative solutions to optimize processes and deploy production-scale solutions.
• Work on challenging and research-based initiatives using advanced machine learning methods focusing on tangible outcomes.
• Prototype and build Generative AI tools to augment our internal processes and drive efficiencies.

What program/technology/software knowledge is essential for this role?

Python; Langgraph/Llamaindex; MCPs; SQL; Docker

Must-have Skills:

• Experience using Python and SQL
• Comfortable using state of the art frameworks such as Langgraph and Llamaindex.
• Advanced programming skills (python, PySpark) and experience in developing machine learning models using supervised and unsupervised approaches; experience with data preprocessing, feature and representation learning, anomaly/outlier detection.
• Curiosity, strong data sense, critical thinking, and technical documentation skills.
• A passion for simplifying and automating work, making things better, continuous learning, solving open-ended problems, improving efficiency, and helping others.
• Strong communication skills with ability to work cross-functionally to articulate, measure and solve issues.

Nice-to-have Skills

• Experience using Docker
• Experience working within the audit and risk domains.

Soft kills :

Communication, relationship building, and liaising with non-technical staff including executives

Education:

• Master degree – comsci, math/stats, engineering, --or-- thesis-based Master in basic sciences like physics, biology, or computational sciences
• At least an undergraduate degree (PhD. or Masters preferred) in a quantitative field (Engineering, Statistics, Mathematics, Computer Science, Economics, Sociology, Psychology etc.).