Role: AI Scientist (Machine Learning Engineer)

Location: Toronto, ON

Job Type: Full-Time

Total Experience: 8+ years

Role Description:

• Design and develop ML systems: Choose the right algorithms, designing data pipelines, and building scalable models.

• Data preparation and feature engineering: Gather, clean, and transform data into a usable format for training models.

• Model training and evaluation: Train models on the prepared data, evaluating their performance using metrics, and fine-tuning them to achieve optimal results.

• Model deployment and monitoring: Deploy models into production on-prem or on cloud infrastructure, ensuring their reliability and performance, and monitoring their performance over time.

• Collaboration: Work with data scientists, software engineers, and domain/business experts to develop and implement ML solutions.

Required Skill Set:

• Strong experience with Deep Learning and NLP

• Hands on working knowledge of interfacing with LLM APIs and LLM agents, prompts and responses

• Strong programming skills in languages such as Python, java, or C++. Experience with cloud platforms (AWS, GCP, and Azure) and MLOps tools.

• 3+ years of hands-on experience in machine learning development, and experience leading a team of machine learning developers or engineers.

• Strong understanding of software development principles, including design patterns, testing, and deployment.

• Experience with DevOps practices such as CI/CD, experience with containerization using Docker and Kubernetes.

• Strong understanding of application implementation requirements, including risk, privacy, and compliance.

• Excellent communication and leadership skills, with the ability to work effectively with cross-functional teams

• Previous experience with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, Flyte, or MetaFlow;

• Familiarity with machine learning frameworks such as PyTorch, TensorFlow and/or similar orchestration and warehousing platforms

• Understanding of CI/CD principles, version control, and production deployment best practices