Data Scientist

Location: Ahmedabad, India

Department: Engineering

Experience: 2-4

• Contribute to the development of AI-powered features across multiple applications.
• Build, train, evaluate, and optimize machine learning models for business use cases.
• Assist in developing Generative AI applications using Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG) techniques.
• Design, develop, and maintain REST APIs and backend services to expose AI/ML capabilities using frameworks such as FastAPI or Flask.
• Perform exploratory data analysis (EDA), statistical analysis, feature engineering, and data preprocessing to support AI and analytics initiatives.
• Work with structured, semi-structured, and unstructured data to enable various AI use cases.
• Translate business requirements into practical, scalable AI/ML solutions.
• Implement data validation, feature engineering, and data quality checks to improve model performance.
• Support model experimentation, evaluation, hyperparameter tuning, and optimization using appropriate performance metrics.
• Assist in integrating AI models into production applications and support deployment activities.
• Monitor model performance, troubleshoot issues, and recommend improvements for accuracy, scalability, and reliability.
• Document experiments, model behavior, evaluation results, technical workflows, and deployment processes.
• Collaborate with AI/ML Leads, software engineers, data engineers, and product teams on solution design, model improvements, and system integration.
• Stay updated with advancements in Machine Learning, Deep Learning, Generative AI, and modern AI frameworks.
• Participate in code reviews, continuous learning, and internal knowledge-sharing initiatives.

• 2+ years of experience as a Data Scientist, Machine Learning Engineer, or AI Developer.
• Strong programming skills in Python.
• Hands-on experience with Python libraries such as NumPy, Pandas, Scikit-learn, and data visualization libraries (Matplotlib, Plotly, or similar).
• Good SQL skills for querying, transforming, and analyzing structured data.
• Strong understanding of machine learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, cross-validation, and hyperparameter tuning.
• Good understanding of statistics, probability, hypothesis testing, and data analysis techniques.
• Experience designing and developing REST APIs using FastAPI or Flask.
• Familiarity with version control (Git), debugging, logging, and software engineering best practices.
• Exposure to Generative AI, Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, or vector databases is an advantage.
• Ability to work independently on data science tasks while collaborating effectively within cross-functional teams.
• Good understanding of handling structured, semi-structured, and unstructured data.
• Strong analytical, problem-solving, and communication skills.
• Eagerness to learn and adapt quickly in a fast-evolving AI landscape.

• Experience with PyTorch, TensorFlow, or other deep learning frameworks.
• Experience with Docker and containerized deployments.
• Exposure to cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Familiarity with AI frameworks such as LangChain, LangGraph, LlamaIndex, or similar.
• Knowledge of vector databases such as FAISS, Chroma, Pinecone, or Milvus.
• Understanding of MLOps concepts, including MLflow, experiment tracking, model versioning, and CI/CD pipelines.
• Exposure to NLP, computer vision, recommender systems, or time-series forecasting.
• Basic knowledge of distributed data processing frameworks such as Apache Spark is a plus.