Sr. AI Engineer to develop and deploy AI/ML Solutions for our Pensions Client

1 week ago

Toronto, Canada S.i. Systems Full time
Sr. AI Engineer to develop and deploy AI/ML Solutions for our Pensions Client

Location: Downtown Toronto (2 Days in Office)

The AI Engineer will play a crucial role in developing and deploying AI/ML solutions for various applications in investment management at HOOPP. The successful incumbent will work closely with cross-functional teams to understand requirements, prototype solutions, and deploy models into production environments. Your expertise in data analysis, AI/ML modeling, and quantitative modeling will drive our innovation and success.

Must Have Skills: A master's or

Ph.D. in a quantitative field. 9+ years of overall experience

including grad school. 5+ years of experience in building production-level

AI/ML and other quantitative models. Experience in investment management or related financial domains is preferred. Familiarity with distributed computing tools and cloud platforms

(AWS, Azure, GCP). Proficiency in

Python, R,

or an object-oriented programming language.

What you will do:

Data Analysis & Processing Evaluate and clean data sets from various sources (SQL databases, NoSQL databases, graph databases, documents, corpora) to ensure they are ready for AI/ML modeling. Proficiency in data preprocessing techniques such as handling missing data, outlier detection, normalization, and transformation to ensure data readiness for modeling. Integrate and merge disparate datasets to create unified datasets suitable for AI/ML modeling. AI/ML and Quantitative Modeling Develop custom AI/ML or statistical models tailored to specific use cases in investment management. Evaluate, fine-tune, and deploy open-source AI/ML models for various applications in investment management. Experience with several of the following frameworks: Pandas, NumPy, SKLearn, XGBoost, PyTorch, TensorFlow, Keras. Prototyping & Deployment Proficiency in prototyping lightweight AI/ML solutions to quickly validate hypotheses and demonstrate feasibility. Develop wrapper APIs for model integration and interaction with other systems. Integrate AI/ML models with existing systems and databases, ensuring seamless functionality and performance. Performance Improvement Monitor the performance of AI/ML models and make adjustments to improve accuracy and efficiency. Identify and design performance metrics for models to monitor, improve, and adjust models to maintain or enhance accuracy, efficiency, and reliability. Product Discovery Collaborate with business stakeholders, particularly Investment and Risk Management teams, to scope out modeling requirements and success metrics. Research & Innovation Stay up-to-date with the latest research and advances in AI/ML and its applications to investment management. Apply innovative techniques to a variety of use cases in investment management. Commitment to staying updated with the latest research trends, advancements, and best practices in AI/ML relevant to investment management. Apply innovative AI/ML techniques and approaches to solve complex challenges and explore new opportunities in investment management. Collaboration Experience working collaboratively with data engineers, software engineers, and teams in investment management and risk management to develop AI/ML solutions. Strong interpersonal skills and ability to work effectively in multidisciplinary teams, contributing to shared goals and outcomes. Communication Clear and concise communication skills to articulate complex AI/ML concepts, methodologies, and results to non-technical stakeholders and team members. Capability to present findings, insights, and recommendations from AI/ML experiments in a compelling and understandable manner. Documentation Document AI/ML experiments, methodologies, findings, and insights systematically for future reference and knowledge sharing. Our Technology Stack Programming Languages:

Python (primary for AI/ML), SQL (data querying), and/or proficiency in some object-oriented language. AI/ML Frameworks and Libraries:

TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, Pandas, NumPy. Data Storage and Management:

SQL Databases (MySQL, PostgreSQL, MS SQL Server, Snowflake) and NoSQL Databases (MongoDB, Cassandra). Cloud Platforms and Big Data:

AWS (EC2, S3, Lambda, SageMaker, Glue, Athena). Containerization, Orchestration, and Development Tools:

Docker, Kubernetes, Flask, FastAPI, Django, GitHub, GitHub Actions, MS DevOps,, Jupyter Notebooks, Anaconda, VSCode/PyCharm. Apply

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