[Remote] MLOps Engineer – Machine Learning Operations & AI Infrastructure (34639)

Descrição da Vaga

Requisitos

• 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 GCP
• Experience building and maintaining production ML services and pipelines
• Strong communication and collaboration skills
• 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

Responsabilidades

• The role involves designing, building, deploying, and automating reliable ML workflows across various platforms to enable efficient delivery of production-ready models
• 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