AI ML Engineer

Position: AI ML Engineer

Location: Mississauga Hybrid 3 days/week

Strong data science exp SQL coding hands on experience and ML/LLM models

Responsibilities:
As part of the Global Reconciliation Utility (GRU) and Data Engineering team this role will be responsible for development and implementation of GRU ML models strategies.
Lead ideation technical development and launch of innovative AI ML models and ensure the design and usage adheres to the overall Citis architecture blueprint.
Undertake preprocessing of structured and unstructured data
Analyze substantial amounts of information to discover trends and patterns.
Build predictive models and machine-learning algorithms.
Combine models through ensemble modeling.
Present information using data visualization techniques.
Propose solutions and strategies to business challenges.
Collaborate with engineering and operational teams to ensure ML Models success and integration.
Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions.
Define ML Models roadmaps and schedules in collaboration with technical and business teams.
Facilitate the creation and maintenance of proper ML Models documentation.
Foster strong relationships with key stakeholders and external partners to support ML Models development.

Qualifications:

AI/ML Development and Big Data experience
Expertise with data reconciliations data quality large data processing and controls
Expertise leading large scale Enterprise Transformation initiatives with a clear track record of success.
Ability to articulate intricacies of Technology Architecture
Deep functional and technical experience in Capital Markets across Front Office Middle Office Operations Risk & Finance.
Demonstrated leadership management and development skills.
Experience with predictive analytics using large and complex datasets.
Enthusiastic about AI and ML optimizations and open-source technologies

Skills required:
Strong problem-solving skills with an emphasis on ML Models development.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (clustering decision tree learning artificial neural networks etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression properties of distributions statistical tests and proper usage etc.) and experience with applications.
Excellent written and verbal communication skills for coordinating across teams.
A drive to learn and master innovative technologies and techniques.
familiar with the following software/tools:
o Knowledge and experience in statistical and data mining techniques: GLM/Regression Random Forest Boosting Trees text mining etc.
o Experience querying databases and using statistical computer languages: R Python SQL.
o Experience using web services: Redshift S3 Spark
o Experience creating and using advanced machine learning algorithms and statistics: regression simulation scenario analysis modeling clustering decision trees neural networks etc.
o Experience with distributed data/computing tools: Map/Reduce Hadoop Hive Spark MySQL /
o Experience with BI tools: Tableau QlikView.