1 month ago
Toronto, CanadaMid Level / Senior
Responsibilities
- Develop end-to-end AI/ML pipelines covering data preprocessing, feature engineering, validation, and deployment.
- Fine-tune LLM-based applications using LangChain, LangSmith, or OpenAI APIs.
- Deploy models into production and build scalable microservices or APIs for real-time model serving.
- Monitor model drift, performance, and reliability while ensuring data quality, governance, and security.
- Work with structured and unstructured datasets and collaborate with data engineering teams on data requirements.
- Prototype AI solutions, evaluate new frameworks and techniques, and explore LLM, vector embedding, multimodal AI, and model optimization advancements.
- Translate business problems into AI solutions and communicate technical concepts to non-technical stakeholders.
Requirements
- 3–5 years of total experience.
- Strong programming skills in Python and experience with NumPy, Pandas, Scikit-learn, PyTorch, or TensorFlow.
- Experience with LLMs, NLP, or deep learning.
- Knowledge of MLflow, Kubeflow, Airflow, Docker, and Kubernetes.
- Experience with AWS, GCP, or Azure and production model deployment.
- Understanding of data structures, algorithms, and software engineering best practices.
- Ability to design scalable, maintainable systems and collaborate in cross-functional teams.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field is preferred.
- Experience with large-scale AI systems, enterprise-grade ML applications, regulated industries, vector databases, RAG systems, or generative AI applications is preferred.
Benefits
- Base salary and variable compensation with health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and recognition programs.
- Regular development conversations, training programs, mentoring programs, and access to an online learning platform.
- Training and onboarding sessions are provided.
- Work location is Toronto, Ontario, Canada, with 37.5 hours per week.
- Accessibility accommodations are available throughout the interview process.
Tech Stack
Apache AirflowApache SparkAWSAzureDockerGoogle Cloud PlatformKubernetesMLflowNumPyPandasPythonPyTorchscikit-learnSQLTensorFlow
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