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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

S.i. SystemsToronto
30+ days ago
Job type
  • Permanent
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.

    • 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.