MLOps Engineer

Key Responsibilities Develop and deploy machine learning applications for prediction, recommendation, text analytics, computer vision, bots, and document intelligence. Design and maintain infrastructure for dataset ingestion, normalization, and combination to derive actionable insights. Deploy and validate machine learning models using frameworks like TensorFlow, PyTorch, Keras, Spacy, and scikit-learn. Utilize Azure ML Studio and Azure Kubernetes Service for scalable model inferencing and deployment. Automate deployments using Terraform and DevOps principles, ensuring high-quality model validation and quality control. Collaborate with cross-functional teams to ensure client satisfaction and project success. Requirements 8+ years of experience in MLOps and cloud-based machine learning deployments. Hands-on expertise with Azure ML Studio, Azure Kubernetes Service, and Terraform for infrastructure as code. Proficiency in Python and open-source ML frameworks (TensorFlow, PyTorch, Keras, Spacy, scikit-learn). Experience with model inferencing, validation, and deployment pipelines. Strong understanding of DevOps principles and automated deployment workflows. #J-18808-Ljbffr