Position : ML Engineer/Data Scientist.

Notice Period : Immediate Joiner.

Total Experience : 5 - 10 Years.

Key Responsibilities :

- Collect, clean, and preprocess data from diverse sources to ensure quality and accuracy.

- Develop and refine statistical models and Machine Learning (ML) algorithms to solve real-world problems.

- Collaborate with cross-functional teams (e.g., data engineering, product, and business) to identify and understand analytical needs.

- Perform exploratory data analysis (EDA) to uncover patterns, trends, and relationships in datasets.

- Evaluate model performance using appropriate metrics, and iterate to optimize accuracy and efficiency.

- Document analyses, methodologies, and best practices to maintain clear records for future reference.

- Stay up-to-date with emerging AI/ML technologies and actively explore new approachesespecially in the areas of deep learning, Generative AI, and Large Language Models (LLMs).

- Present findings and insights to both technical and non-technical stakeholders in a clear, concise manner.

Qualifications & Data Engineering:

- Design and Build Data Pipelines: Assist in the development of reliable, efficient ETL (Extract, Transform, Load) and/or ELT pipelines that move data from a variety of internal and external sources into data storage systems.

- Data Integration and Collaboration: Work closely with data scientists, data analysts, and software engineers to ensure data is readily available and properly structured for analysis and modeling.

- Data Quality and Validation: Implement quality checks, error handling, and monitoring processes to ensure the reliability and accuracy of data.

- Optimize Performance: Contribute to the optimization of data pipelines and database queries to handle high-volume data and achieve better performance and scalability.

- Maintain Documentation: Develop and maintain technical documentation for data systems, pipelines, and processes to keep the team aligned and informed.

- Security and Compliance: Support efforts to ensure secure data handling and compliance with relevant regulations and industry best practices.

- Continuous Learning and Innovation: Stay informed about emerging data engineering tools, techniques, and technologies; share learnings with the team; and implement improvements where applicable.

Tech Stack : Python, SQL, ML, AI, LLM, Deep Learning, Generative AI.