14-15 years of total experience with at least 6 years relevant experience
Proven background in solutions architecture or equivalent
Experience with AI/ML, MLOps, data engineering, and fine-tuning ML models
Expertise in advanced analytics tools (Python, R) and deep learning frameworks (like TensorFlow)
Knowledge in ML life cycle best practices and feature engineering
Familiarity with data governance and architecture for ML workloads
Experience in 2 industry verticals such as Life Science or BFSI preferred.
Responsibilities
Lead the design of AI architecture and select appropriate technologies
Manage and mentor a team of data scientists to promote collaboration
Align technical solutions with stakeholder inputs and evolving requirements
Develop and implement AI and ML models in enterprise applications
Establish best practices for model management and MLOps
Evaluate and leverage advanced analytics tools and methodologies
Guide the integration of ML and deep learning workflows in production.
Benefits
Opportunity to work with cutting-edge AI/ML technologies
Mentorship role fostering professional growth
Exposure to varied industry verticals
Potential for collaboration across teams
Flexibility to influence technology choices and architecture.
Full Job Description
Total 14 to 15 Years of experience - 6B Grade
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
