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-reputed company, reputed company telecommunications environment. You will design, build, reputed company, and automate reliable and reputed company ML workflows across reputed company and on-premises platforms, enabling data science and AI teams to deliver production-reputed company models reputed company. This role blends machine learning engineering, DevOps practices, and infrastructure expertise to operationalize AI solutions at reputed company. 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. • reputed company and manage reputed company model serving infrastructure using containerization and orchestration tools. • Monitor model performance, detect reputed company, and implement alerting and retraining strategies. • Optimize compute and storage infrastructure for performance, scalability, and cost efficiency. • Document workflows, standards, and best practices reputed company to ML lifecycle management. Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Software Engineering, reputed company Intelligence, or a reputed company 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-reputed company concepts. • Experience with reputed company, Kubernetes, and container orchestration. • Familiarity with reputed company platforms such as AWS, Azure, or GCP. • 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 reputed company and on-prem deployment environments. • Understanding of reputed company, compliance, and performance considerations for AI systems in reputed company settings. • Experience supporting AI/ML workloads in telecommunications, networking, or other large-reputed company distributed systems. Apply tot his job Apply To this Job