MLOps Engineer

Title: ML Ops Engineer

Mode: Contract

Location: Toronto ON - Hybrid

Mandatory Skills : Model Life Cycle Management LLMOps GCP Vertex AI Terraform GCP-DevOps Service DeepChecks CloudFormation

Role Summary

• The ML Ops Engineer will be responsible for designing building and maintaining the infrastructure and processes for deploying and managing machine learning models in production

Responsibilities

• Proficient in Python scripting and familiarity with Python packaging eg PyPI pip virtualenv
• Hands on experience with ML workflow tools like MLflow Kubeflow MLRun DVC Airflow leveraging Python integrations
• Automate model training testing and deployment processes using CICD tools
• Experience with cloud platform preferably GCP and their Python SDKs
• Experience with SQL and Pythonbased ETL processes
• Familiarity with data processing frameworks like Apache Spark or Dask using PySpark or similar Python interfaces
• Collaborate with development teams to test optimize ML workflows and deployintegrate machine learning models into applications
• Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements
• Translate machine learning algorithms into productionlevel code
• Knowledge of version control systems eg git and collaborative coding practices
• Monitor the performance of deployed models track data or concept drift and update or retrain models as needed
• Ensure adherence to performance standards and compliance with data security requirements
• Keep abreast with new tools algorithms and techniques in machine learning and work to implement them in the organization

Experience

• 5-7 years of experience in developing and deploying enterprisescale machine learning solutions in a software engineering with a focus on Python
• Experience with REST API development in Python eg Flask FastAPI for model serving
• Experience in developing trouble shooting and resolving issues related to ML systems in production
• Understanding of DevOps principles and exposure to architectural patterns of largescale software applications

Education

• A bachelors degree in computer science data science applied mathematics software engineering or related masters degree preferred