Job Description
- 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
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