15 years of total experience in technology and data science fields.
6-7 years of relevant solutions architecture experience.
Proficiency in AI/ML and related technical domains.
Experience managing and mentoring data science teams.
Strong understanding of ML workflows and architecture.
Familiarity with advanced analytics tools including Python and R.
Experience with a variety of machine learning models and their practical applications.
Responsibilities
Lead the design and development of AI architecture for projects.
Align technical implementations with stakeholder requirements.
Manage a team of data scientists, promoting collaboration and innovation.
Develop and integrate AI/ML models in enterprise applications.
Establish best practices for the ML lifecycle and MLOps.
Coordinate estimation and proposal creation for solutions.
Select technologies for ML systems from open-source and commercial offerings.
Benefits
Opportunities for professional growth and further training in emerging technologies.
Flexible working arrangements to support work-life balance.
Access to advanced analytics tools and resources for skill development.
Collaborative work environment that emphasizes continuous learning.
Full 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
