Sr. Solution Architect – Advanced Analytics - Remote
• Total 14 to 15 Years of experience
• Relevant experience 6 to 7 Years
• Experience in solutions architecture or equivalent experience
• Experience in technical domains such as AI/ML, multimodal ML, model evaluation, MLOps, MLSecOps, ML training, inference, data engineering, data science, fine-tuning
• Manage and mentor a team of skilled data scientists, fostering a culture of collaboration, innovation, and continuous learning.
• Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders
• Take the lead in designing the AI architecture and selecting technologies from both open-source and commercial offerings.
• Knowing the workflow and pipeline architectures of ML and deep learning workloads, including the components and trade-offs across data management, governance, model building, deployment, and production workflows, is crucial.
• Experience in advanced analytics tools (Python, R) along with applied mathematics, ML, Deep Learning frameworks (such as TensorFlow), and ML techniques (such as random forest and neural networks).
• Experience in Estimation and Proposal creation
• Experience in Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, Deep Neural Networks,..)
• Developing AI and ML models in real-world environments, and integrating AI and ML using cloud-native or hybrid technologies into large-scale enterprise applications.
• Experience in developing best practices for the ML life cycle, feature engineering, model management, MLOps, deployment, and monitoring.
• Knowledge and experience in working on Data Warehouse, Data Lake, and Reporting
• Experience in any 2 verticals (Life science, manufacturing, BFSI, Energy and Utilities) is desired
Apply Now
Apply Now
• Relevant experience 6 to 7 Years
• Experience in solutions architecture or equivalent experience
• Experience in technical domains such as AI/ML, multimodal ML, model evaluation, MLOps, MLSecOps, ML training, inference, data engineering, data science, fine-tuning
• Manage and mentor a team of skilled data scientists, fostering a culture of collaboration, innovation, and continuous learning.
• Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders
• Take the lead in designing the AI architecture and selecting technologies from both open-source and commercial offerings.
• Knowing the workflow and pipeline architectures of ML and deep learning workloads, including the components and trade-offs across data management, governance, model building, deployment, and production workflows, is crucial.
• Experience in advanced analytics tools (Python, R) along with applied mathematics, ML, Deep Learning frameworks (such as TensorFlow), and ML techniques (such as random forest and neural networks).
• Experience in Estimation and Proposal creation
• Experience in Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, Deep Neural Networks,..)
• Developing AI and ML models in real-world environments, and integrating AI and ML using cloud-native or hybrid technologies into large-scale enterprise applications.
• Experience in developing best practices for the ML life cycle, feature engineering, model management, MLOps, deployment, and monitoring.
• Knowledge and experience in working on Data Warehouse, Data Lake, and Reporting
• Experience in any 2 verticals (Life science, manufacturing, BFSI, Energy and Utilities) is desired
Apply Now
Apply Now