REQUIRED SKILLS & QUALIFICATIONS:

• Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, AI/ML, or related field.
• Proven experience as a Data Scientist with exposure to ML and NLP projects.
• Strong hands-on experience with Databricks on Azure (MLflow, Delta Lake, Databricks ML).
• Proficiency in PySpark for large-scale data processing.
• Experience in training, fine-tuning, and deploying LLMs within Databricks environment.
• Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn, Hugging Face).
• Solid understanding of data science workflows: data wrangling, feature engineering, model development, and evaluation.
• Working knowledge of Azure cloud services (Azure Data Lake, Azure Synapse, Azure ML).
• Strong problem-solving, analytical thinking, and communication skills.

GOOD-TO-HAVE SKILLS:

• Experience with MLOps practices and tools (CI/CD for ML, MLflow).
• Knowledge of vector databases and LLM deployment pipelines.
• Familiarity with prompt engineering and RAG (Retrieval-Augmented Generation) techniques.
• Exposure to generative AI projects on cloud platforms.

ABOUT THE ROLE:

We are seeking a highly motivated and skilled Data Scientist with strong expertise in data science fundamentals, machine learning (ML), and large language models (LLMs). The ideal candidate will have hands-on experience working with Databricks and Azure ecosystems, including PySpark for data processing and LLM tuning within Databricks. This role involves building and optimizing data science solutions that leverage cloud-based technologies to deliver business value.

KEY RESPONSIBILITIES:

• Design, develop, and deploy data science and ML solutions on Databricks (Azure environment).
• Work on end-to-end ML lifecycle, from data preparation and feature engineering to model training, evaluation, and deployment.
• Apply LLM fine-tuning and optimization techniques within Databricks for domain-specific use cases.
• Utilize PySpark for distributed data processing, cleaning, and transformation.
• Collaborate with data engineers, cloud architects, and business stakeholders to ensure seamless integration of ML models into production workflows.
• Conduct exploratory data analysis (EDA), statistical modeling, and hypothesis testing to extract insights from structured and unstructured data.
• Stay updated on the latest advancements in AI/ML, LLMs, and Databricks capabilities to bring innovative solutions.
• Document methodologies, experiments, and best practices for knowledge sharing.