Myticas LLC

Compensation

Full Description

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.