Join a dynamic team to drive the development and deployment of advanced machine learning solutions at scale. This role focuses on building robust deep learning models, optimizing production ML services, and implementing modern MLOps practices to ensure high performance, reliability, and scalability. You will work closely with cross-functional teams to deliver impactful AI-driven features and maintain seamless integration with backend systems.

Responsibilities

- Design, develop, and deploy deep learning models using Python, PyTorch, and TensorFlow
- Build and maintain scalable data pipelines and APIs for production ML services
- Implement and manage MLOps workflows, including model monitoring, versioning, and CI/CD automation
- Optimize inference performance, reliability, and latency across cloud infrastructure (AWS, GCP)
- Collaborate with product, data, and engineering teams to define technical requirements and deliverables
- Conduct code reviews, contribute to technical design discussions, and ensure best practices in ML engineering
- Troubleshoot and resolve issues in production ML systems, ensuring high availability and robustness

Skills And Requirements





- Minimum 6 years of experience deploying machine learning solutions in production environments
- Advanced proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow)
- Strong background in MLOps, including Docker, Kubernetes, and CI/CD pipelines
- Hands-on experience with cloud platforms (AWS, GCP) and scalable backend integrations
- Proficient in SQL and data engineering for large-scale data processing
- Demonstrated ability to optimize model inference and system performance
- Excellent communication skills and experience collaborating with distributed teams
- Strong problem-solving skills and attention to detail

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📌 AI Engineer – PyTorch & MLOps (Contract) (Canada)
🏢 Twine
📍 Canada