Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
Mapping decisions with requirements and be able to translate the same to developers.
Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
Defining guidelines and benchmarks for NFR considerations during project implementation.
Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers.
Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it.
Understanding and relating technology integration scenarios and applying these learnings in projects.
Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Total experience 11+ years.
Hands on working experience in data science with a focus on predictive modelling (eg. Statistical Analysis, ANOVA, Time series Analysis, Regression Analysis) and optimization.
Strong working experience with Data Science on GCP, Generative AI fundamentals.
Strong experience with Python (and/or R) and libraries such as Pandas, NumPy, Scikit-learn.
Strong knowledge of Cloud Architecture and deployment of data solutions in cloud ecosystems (e.g. AWS, Azure, GCP).
Proven expertise in machine learning, mathematical optimization (e.g., LP, IP, genetic algorithms, RL), and NLP techniques.
Familiarity with Generative AI fundamentals and hands-on experience with RAG, Lang Chain, Llama Index, and prompt engineering.
Strong understanding of MLOps principles and tools for CI/CD, monitoring, and model lifecycle management.
Excellent communication skills and the ability to collaborate effectively with cross-functional teams.

Principal Engineer, Data Science

Key skills