Full-Time Associate Principal Engineer, Data Science

• Total experience 11+ years.
• Hands on working experience in data science with a focus on predictive modeling 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, TensorFlow, PyTorch.
• Strong knowledge of Cloud Architecture and deployment of data solutions in cloud ecosystems (e.g. 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.
• Bachelor’s or master’s degree in computer science, Information Technology, or a related field.

• Writing and reviewing great quality code
• 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 clients’ 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.

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