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

S.i. Systems; Toronto; On-site; CAD 120,000 - 160,000; Full time

Job summary

A leading company in investment management is seeking a Sr. AI Engineer to develop and deploy AI/ML Solutions. This role involves collaboration with cross-functional teams to create innovative models and improve investment strategies. The ideal candidate holds a Master’s or Ph.D. in a quantitative field and has extensive experience in AI/ML modeling. Additional skills in programming, cloud platforms, and data analysis are essential for success in this position.

Qualifications

• 9+ years of overall experience including grad school.
• 5+ years in production-level AI / ML and quantitative models.
• Experience in investment management preferred.

Responsibilities

• Develop custom AI / ML models for investment management applications.
• Evaluate and clean datasets for modeling.
• Monitor and improve the performance of AI / ML models.

Skills

Data Analysis

AI / ML Modeling

Quantitative Modeling

Communication

Education

Master's or Ph.D. in a quantitative field

Tools

Python

R

SQL

TensorFlow

PyTorch

Keras

Pandas

NumPy

Docker

Kubernetes

Job description

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.
• 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.
• 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.
• 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.