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

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.