Data Scientist

Roles and Responsibilities Develop, maintain and enhance Python-based applications, ML services and AI solutions.

Design and implement scalable, reusable and production-ready software components using Python and open-source AI/ML frameworks.

Develop and deploy machine learning and deep learning models for real-world business use cases.

Work with Generative AI and Large Language Models (LLMs), including RAG, prompt engineering, model evaluation, fine-tuning and knowledge-based AI solutions.

Develop AI/ML pipelines for classification, regression, forecasting, NLP and other machine learning use cases.

Work with frameworks and libraries such as PyTorch, LangChain, LlamaIndex, Hugging Face, Scikit-learn, Pandas and Spark.

Design and implement RAG-based solutions and LLM applications using enterprise and customer-specific data.

Develop APIs and microservices for AI/ML solutions using technologies such as FastAPI.

Work with structured and unstructured data using RDBMS, NoSQL and distributed data-processing platforms.

Develop data processing and engineering pipelines using technologies such as Apache Spark / PySpark.

Build and maintain ML training, model evaluation and deployment pipelines.

Skills / Requirements Strong hands-on experience with Python.

Good understanding of software engineering principles and development of scalable, reusable applications.

Experience with REST APIs and/or microservices, preferably using FastAPI.

Strong understanding of machine learning and deep learning concepts.

Hands-on experience with PyTorch and/or TensorFlow.

Experience with machine learning techniques including classification, regression, forecasting, NLP and deep learning.

Experience developing and deploying production-grade ML models.