Lead the design and deployment of scalable AI infrastructure with a focus on MLOps, machine learning standardization, GPU/cloud setup, and data integration. Collaborate across teams to deliver robust, efficient AI solutions. Key Responsibilities: Architect and develop AI platforms ensuring scalability and reliability Optimize MLOps pipelines for model development and deployment Standardize ML practices across the lifecycle Manage GPU and cloud resources to support AI workloads Design and implement data integration pipelines Collaborate with cross-functional teams to deliver end-to-end solutions Mentor engineers and foster technical excellence Stay current with AI/ML advancements to drive innovation Qualifications: 8-10 years in AI/ML engineering, delivering complex projects Expertise in ML frameworks (TensorFlow, PyTorch), MLOps tools (MLflow, Kubeflow), and cloud platforms (AWS, Azure) Proficiency in Python, Java, or C++ Strong background in GPU computing and data engineering Excellent problem-solving and communication skills