Designation: Consultant

Experience: 5 to 7 Years

Location: Toronto, Ontario , Canada

Job Description:

We are seeking a driven Machine Learning Engineer to design, build, and scale our next-generation AI and analytics platforms. In this role, you will bridge the gap between data science and production engineering, leveraging the Databricks ecosystem to deploy robust ML models, explore cutting-edge NLP/GenAI applications, and empower the business with self-service analytics. If you love optimizing workflows and turning complex data into intelligent, real-world solutions from your Canadian home office, we want to hear from you.

Key Responsibilities:

End-to-End ML Development: Design, build, and deploy scalable machine learning solutions, NLP applications, and Generative AI (GenAI) frameworks.

Pipeline Engineering: Develop and manage production-grade ML pipelines using Databricks, Apache Spark, and MLflow for seamless model tracking and deployment.

Self-Service Analytics: Configure and optimize Databricks Genie to democratize data insights and enable automated, natural-language data discovery across teams.

Workflow Optimization: Maintain, monitor, and continuously improve existing production ML workflows, ensuring high availability, speed, and reliability.

Collaboration: Work closely with data scientists, data engineers, and business stakeholders to translate complex requirements into robust data products.

Skills:

Strong expertise in Python, SQL, Machine Learning, Statistical Modeling, Databricks, Apache Spark, MLflow, and MLOps, with preferred experience in GenAI/LLMs, Prompt Engineering, Semantic Search, Databricks Genie, Retail Analytics, and cloud platforms such as AWS, Azure, or GCP.