MLOps Engineer - Machine Learning Operations & AI Infrastructure (34639)
We are seeking a skilled MLOps Engineer to support the full lifecycle of machine learning and AI solutions in a large-scale, enterprise telecommunications environment. You will design, build, deploy, and automate reliable and scalable ML workflows across cloud and on-premises platforms, enabling data science and AI teams to deliver production-ready models efficiently. This role blends machine learning engineering, DevOps practices, and infrastructure expertise to operationalize AI solutions at scale.
Key Responsibilities
• Design and maintain end-to-end MLOps pipelines supporting model training, validation, deployment, monitoring, and automated retraining.
• Collaborate with data scientists, AI developers, and software engineering teams to transition models from research to production.
• Implement CI/CD pipelines for machine learning workflows, including automated testing and artifact management.
• Manage model versioning, experiment tracking, and governance to ensure reproducibility and auditability.
• Deploy and manage scalable model serving infrastructure using containerization and orchestration tools.
• Monitor model performance, detect drift, and implement alerting and retraining strategies.
• Optimize compute and storage infrastructure for performance, scalability, and cost efficiency.
• Document workflows, standards, and best practices related to ML lifecycle management.
Required Qualifications
• Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• 3+ years of experience in MLOps, DevOps, or ML Engineering roles.
• Strong programming skills in Python; familiarity with Java or similar languages is an asset.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Deep understanding of CI/CD, automation tools, and infrastructure-as-code concepts.
• Experience with Docker, Kubernetes, and container orchestration.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience building and maintaining production ML services and pipelines.
• Strong communication and collaboration skills.
Preferred Qualifications
• Experience with MLOps and experiment-tracking tools such as MLflow, Kubeflow, Airflow, or DVC.
• Knowledge of feature stores, metadata management, and model governance frameworks.
• Familiarity with hybrid cloud and on-prem deployment environments.
• Understanding of security, compliance, and performance considerations for AI systems in enterprise settings.
• Experience supporting AI/ML workloads in telecommunications, networking, or other large-scale distributed systems.
Apply Now
Apply Now
Key Responsibilities
• Design and maintain end-to-end MLOps pipelines supporting model training, validation, deployment, monitoring, and automated retraining.
• Collaborate with data scientists, AI developers, and software engineering teams to transition models from research to production.
• Implement CI/CD pipelines for machine learning workflows, including automated testing and artifact management.
• Manage model versioning, experiment tracking, and governance to ensure reproducibility and auditability.
• Deploy and manage scalable model serving infrastructure using containerization and orchestration tools.
• Monitor model performance, detect drift, and implement alerting and retraining strategies.
• Optimize compute and storage infrastructure for performance, scalability, and cost efficiency.
• Document workflows, standards, and best practices related to ML lifecycle management.
Required Qualifications
• Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• 3+ years of experience in MLOps, DevOps, or ML Engineering roles.
• Strong programming skills in Python; familiarity with Java or similar languages is an asset.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Deep understanding of CI/CD, automation tools, and infrastructure-as-code concepts.
• Experience with Docker, Kubernetes, and container orchestration.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience building and maintaining production ML services and pipelines.
• Strong communication and collaboration skills.
Preferred Qualifications
• Experience with MLOps and experiment-tracking tools such as MLflow, Kubeflow, Airflow, or DVC.
• Knowledge of feature stores, metadata management, and model governance frameworks.
• Familiarity with hybrid cloud and on-prem deployment environments.
• Understanding of security, compliance, and performance considerations for AI systems in enterprise settings.
• Experience supporting AI/ML workloads in telecommunications, networking, or other large-scale distributed systems.
Apply Now
Apply Now