Responsibilities :

- Design and implement scalable data science solutions using Azure Data Lake and related Azure services.

- Develop, train, and deploy machine learning models to address business challenges.

- Collaborate with data engineering teams to optimize data pipelines and ensure seamless data integration within Azure cloud infrastructure.

- Conduct exploratory data analysis (EDA) to identify trends, patterns, and insights.

- Build predictive and prescriptive models to support decision-making processes.

- Expertise in developing end-to-end Machine learning lifecycle utilizing crisp-DM which includes of data collection, cleansing, visualization, preprocessing, model development, model validation and model retraining

- Proficient in building and implementing RAG systems that enhance the accuracy and relevance of model outputs by integrating retrieval mechanisms with generative models.

- Strong hands-on experience in designing, training, and fine-tuning Large Language Models (LLMs) for a domain specific application

- Expertise in leveraging both open and closed-source LLMs and their seamless integration into customized RAG-based chatbots, optimizing performance and relevance for specific use cases

- Expertise in working time series analysis and forecasting

- Ensure data security, compliance, and governance within the Azure cloud ecosystem.

- Monitor and optimize model performance and scalability in production environments.

- Prepare clear and concise documentation for developed models and workflows.

Skills Required :

- Good experience in using pandas, matplotlib, seaborn, sklearn, stats models, pycaret and H20.ai etc. for building Machine learning models and Opencv2, Spacy, NLTK, TensorFlow, Keras and FastAI etc. in building Deep learning Models

- Worked and utilized data from various RDBMS like MYSQL, SQL Server, Postgres and NoSQL databases like MongoDB, Cassandra, Redis and graph DB like Neo4j, Grakn.

- Skilled in applying Agile principles to AI/ML projects, ensuring iterative development, continuous feedback, and adaptability to changing requirements

- Proven experience as a Data Scientist with a strong focus on Azure cloud platform and Data Lake architecture.

- Proficiency in Python, R, or other programming languages commonly used in data science.

- Hands-on experience with Azure services such as Azure Data Lake, Azure Synapse Analytics, Azure Machine Learning, Azure Databricks, and Azure Functions.

- Strong knowledge of SQL and experience in querying large datasets from Data Lakes.

- Familiarity with data engineering tools and frameworks for data ingestion and transformation in Azure.

- Solid understanding of machine learning algorithms and statistical modelling techniques.

- Experience with version control systems (e.g., Git) and CI/CD pipelines for machine learning projects.

- Excellent problem-solving skills and the ability to work collaboratively in a team environment.