- 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
