Key Responsibilities :

- Design, develop, and implement scalable AI and machine learning solutions using Databricks.

- Build and maintain end-to-end machine learning pipelines for data preparation, model development, training, validation, and deployment.

- Develop data processing and transformation workflows using PySpark and SQL.

- Perform data exploration, preprocessing, feature engineering, and statistical analysis to prepare datasets for machine learning.

- Develop, train, evaluate, and optimize machine learning models based on business and technical requirements.

- Work with large and complex datasets within the Databricks environment.

- Develop reusable and production-ready Python and PySpark code for data processing and ML workflows.

- Collaborate with data engineers, data scientists, business teams, and technology stakeholders to understand requirements and deliver AI/ML solutions.

- Implement scalable data pipelines and workflows to support machine learning applications.

- Monitor model performance and identify opportunities for model optimization and improvement.

- Troubleshoot data, model, pipeline, and performance-related issues.

- Apply appropriate machine learning algorithms and techniques based on the problem statement and available data.

- Support deployment and operationalization of machine learning models in production environments.

- Follow coding, testing, documentation, and development best practices.

- Contribute to improving the scalability, reliability, and performance of AI/ML solutions.

Technical Skills :

- Strong hands-on experience with Databricks and its capabilities for data engineering and machine learning.

- Strong programming experience in Python.

- Good experience with PySpark for distributed data processing.

- Strong SQL skills for data extraction, transformation, and analysis.

- Solid understanding of Machine Learning and Artificial Intelligence concepts.

- Experience with data preprocessing, feature engineering, model training, evaluation, and optimization.

- Experience working with large-scale datasets and distributed computing environments.

- Exposure to Azure or AWS cloud platforms is preferred.

- Knowledge of MLOps practices and ML model deployment is good to have.

- Understanding of end-to-end ML lifecycle and productionization of models.

MLOps & Cloud Exposure :

- Understanding of practices involved in taking ML models from development to production.

- Exposure to model deployment, monitoring, versioning, and lifecycle management.

- Familiarity with cloud-based AI/ML environments on Azure or AWS.

- Experience implementing automated and scalable ML workflows will be an advantage.

Candidate Profile :

- 10+ years of relevant experience in AI/ML, Data Science, Machine Learning Engineering, or related technology roles.

- Strong problem-solving and analytical skills.

- Ability to work with large datasets and translate complex data problems into scalable solutions.

- Strong understanding of software development and data engineering practices.

- Ability to collaborate effectively with cross-functional teams.

- Good communication skills with the ability to explain technical concepts to business and technical stakeholders.

- Passion for building scalable and production-ready AI/ML solutions using modern data platforms.