Databricks AI/ML Engineer

Job Description

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.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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