Machine Learning Engineer

Role Overview 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. 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. 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.

The role involves designing and deploying scalable machine learning, NLP, and Generative AI solutions using the Databricks ecosystem. You will also optimize production ML pipelines and configure self-service analytics to democratize data insights.

Candidates must have advanced proficiency in Python, SQL, and MLOps practices, with hands-on experience in Databricks and Apache Spark. Experience with LLMs and the retail industry is preferred.

• SQLSouhaitée
• CI/CDSouhaitée
• Apprentissage automatiqueSouhaitée
• PythonSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Python
• SQL
• Machine Learning
• Databricks
• Apache Spark
• MLflow
• MLOps
• NLP
• Generative AI
• LLMs
• Prompt Engineering
• Semantic Search
• Databricks Genie
• CI/CD
• Statistical Modeling
• Distributed Computing

• Data & Analytics
• Technology
• Software
• Engineering
• Retail

Expérience minimale

5+ ans

Postuler avant le

20 sept. 2026

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Niveau d’expérience

Mid-Senior level

Mode de candidature

La candidature directe est offerte