1 month ago
Toronto, CanadaSenior
Responsibilities
- Develop end-to-end AI/ML pipelines covering data preprocessing, feature engineering, validation, deployment, and production monitoring.
- Fine-tune LLM-based applications using LangChain, LangSmith, and OpenAI APIs.
- Build scalable microservices or APIs to serve machine learning models in real time.
- Implement monitoring for model drift, performance, and reliability.
- Collaborate with data engineering teams to define data requirements and ensure data quality, governance, and security.
- Prototype AI solutions, run experiments, and evaluate new AI frameworks, tools, and techniques.
- Work with structured and unstructured datasets using Python, SQL, Spark, and Pandas.
- Partner with product managers, business teams, cloud engineers, and DevOps teams to translate business problems into scalable AI solutions.
- Communicate complex technical concepts to non-technical stakeholders and maintain technical documentation.
Requirements
- At least 7 years of relevant experience.
- Strong programming skills in Python, including NumPy, Pandas, Scikit-learn, and PyTorch or TensorFlow.
- Experience with LLMs, NLP, or deep learning.
- Knowledge of MLOps tools including MLflow, Kubeflow, Airflow, Docker, and Kubernetes.
- Experience with AWS, GCP, or Azure and model deployment.
- Understanding of data structures, algorithms, and software engineering best practices.
- Ability to design scalable, maintainable systems and apply strong problem-solving and analytical skills.
- Strong communication, documentation, and cross-functional collaboration skills.
- Preferred: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- Preferred: Experience building large-scale AI systems or enterprise-grade machine learning applications, especially in regulated industries.
- Preferred: Knowledge of vector databases, RAG systems, or generative AI applications.
Benefits
- Base salary of $125,500 - $154,000 CAD.
- Health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs.
- Regular development and performance conversations, training programs, mentoring programs, and access to an online learning platform.
- Training and onboarding sessions are provided.
- The role is located in Toronto, Ontario, Canada, with a 37.5-hour work schedule.
- Accessibility accommodations are available throughout the interview process.
Tech Stack
Apache AirflowApache SparkAWSAzureDockerGoogle Cloud PlatformKubernetesMLflowNumPyPandasPythonPyTorchscikit-learnSQLTensorFlow
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