AI/ML Engineer

Job Title: AI/ML EngineerLocation: Canada (Hybrid/On-site/Remote options available)Experience Required: 2 – 10 yearsEmployment Type: Full-timeAbout The RoleWe are seeking a skilled AI/ML Engineer to design, develop, and deploy advanced machine learning and artificial intelligence models that power next-generation data-driven solutions. The ideal candidate will have stronganalytical skills, hands-on experience in model development, and the ability to work collaboratively with cross-functional teams to integrate intelligent systems into business applications.Key ResponsibilitiesDesign and implement machine learning, deep learning, and AI-based models to solve complex business problems. Preprocess, clean, and analyze large datasets using Python, R, or Spark. Develop and optimize end-to-end ML pipelines for training, validation, and deployment. Work with cloud platforms such as AWS, Azure, or GCP for scalable model deployment. Apply NLP, computer vision, or predictive analytics depending on project needs. Collaborate with data engineers, software developers, and product teams to integrate models into production environments. Continuously evaluate model performance and fine-tune algorithms for accuracy and efficiency. Stay current with the latest trends and research in AI, ML, and data science. Required Skills & QualificationsBachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or related field. 2–10 years of professional experience in machine learning, data science, or AI model development. Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras. Strong understanding of statistical modeling, feature engineering, and model evaluation. Experience with SQL/NoSQL databases and data pipeline tools (Airflow, Kafka, Spark). Familiarity with MLOps tools like MLflow, Kubeflow, or SageMaker is a plus. Excellent problem-solving, analytical, and communication skills. Preferred QualificationsExperience with LLMs (Large Language Models), transformer architectures, or GenAI applications. Exposure to Docker/Kubernetes for model containerization and deployment. Contributions to open-source AI/ML projects or published research papers. Certification in AI/ML or Cloud Computing (e.g., AWS ML Specialty, Google Professional ML Engineer).