As a data scientist, you will need to build Solutions based on Deep learning, Reinforcement learning, Computer vision, Expert system, Transfer Learning, NLP, and generative models.
To Define, design, and deliver ML architecture patterns operable in native and hybrid cloud architectures.
Implement machine learning algorithms in services and pipelines that can be used on a web scale.
Create demos and proofs of concept, develop AI/ML based products and services.
Creating Functional and technical specifications for AI & ML solutions.
Follow SDLC process
Advanced analytical knowledge of data and data conditioning
Programming advanced computing and developing algorithms
Developing software, data models and executing predictive analysis
Design, develop, and implement generative AI models using state-of-the-art techniques.
Collaborate with cross-functional teams to define project goals, research requirements and develop innovative solutions.
Strong proficiency in Python/R/Scala (Python is a must and R, Scala is a plus).
Strong proficiency in SQL, NO SQL Databases.
Experience in implementing and deploying AI Machine Learning solutions (using various models, such as CNN, RNN, Fuzzy logic, Q learning, SVM, Ensemble, Logistic Regression, Random Forest etc.)
Specializes in at least one of the AI/ML stack, Frameworks, and tools like MxNET and Tensorflow.
Hands-on experience with data analytics and classical machine learning, deep learning tools (e.g. Pandas, NumPy, Scikit-learn) and deep learning frameworks (e.g. Tensorflow, Pytorch).

What will make you stand out

Experience in production software engineering routines in DevOps/MLOps (e.g.Continuous Code Integration and Deployment).
Experiences in cloud-based solutions (e.g. AWS, Azure, GCP).

Experience and Qualification:

B.Tech/M.Tech/Ph.D CS/IS/IT or Msc Statistics, MCA
7+ Yrs core experience in Data Science projects

AI/ML – Architect