LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities, and support artificial intelligence initiatives., LatentView Analytics enables leading global brands to predict new revenue streams, anticipate product trends and popularity, improve customer retention rates, optimize investment decisions, and turn unstructured data into valuable business assets.
Designation : Associate
Location : Toronto, Ontario , Canada (TOR)
Experience : 3 to 6 Years
Job Role :
We are seeking a talented and 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.
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.
Required skills :
Primary Skills (Mandatory) Programming & Querying: Advanced proficiency in Python and SQL for data manipulation and model development. Machine Learning: Strong foundation in core ML algorithms, statistical modeling, and data science principles. Databricks Ecosystem: Hands-on experience building and deploying models within Databricks, utilizing Apache Spark for distributed computing and MLflow for the ML lifecycle. MLOps: Demonstrated experience in ML Ops practices, including model versioning, CI/CD pipelines for ML, automated testing, and production monitoring.
Secondary Skills (Preferred & Nice-to-Have) GenAI & NLP: Experience working with Large Language Models (LLMs), prompt engineering, or semantic search frameworks. Advanced Analytics Configuration: Direct experience or strong familiarity with setting up Databricks Genie spaces. Domain Expertise: Prior experience in the Retail industry or retail analytics (e.g., demand forecasting, customer churn, recommendation engines) is a significant plus. Cloud Platforms: Familiarity with cloud infrastructure (AWS, Azure, or GCP) as it integrates with Databricks.
At LatentView Analytics, we value a diverse, inclusive workforce and provide equal employment opportunities for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual's race, colour, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.
