DataBricks Engineer

We are looking for an experienced AI/ML Engineer with 4-5 years of hands-on experience in designing, building, and deploying machine learning models and AI-driven solutions. The ideal candidate must be highly proficient in Databricks and have strong exposure to building scalable data pipelines, MLOps practices, and productionizing ML models in a cloud environment. The role involves close collaboration with data engineering, product, and business teams to translate business problems into robust, production-grade AI/ML solutions.

Key Responsibilities

1 . Work Experience on the Databricks platform (Notebooks, Delta Lake, MLflow, Databricks Workflows/Jobs) for large-scale data processing and model development.

2. Build and maintain end-to-end ML pipelines (data ingestion, feature engineering, training, validation, and deployment) using Databricks.

3. Design, develop, and deploy machine learning and AI models to solve real-world business problems.

4.Optimize and fine-tune ML models for performance, scalability, and accuracy.

5.Collaborate with data engineers to design and maintain robust, scalable data pipelines feeding ML systems.

6. Implement MLOps best practices, including model versioning, CI/CD for ML, and automated monitoring/retraining pipelines.

7. Conduct exploratory data analysis (EDA), feature engineering, and statistical analysis to support model development.

8. Deploy and monitor models in production, ensuring reliability, performance, and scalability.

9. Work with cloud platforms (Azure/AWS/GCP) for data storage, compute, and ML model deployment.

10. Document processes, models, and architecture; present findings and recommendations to technical and non-technical stakeholders.

11. Stay updated with the latest developments in AI/ML, LLMs, and the Databricks ecosystem, and recommend adoption where relevant.

Required Skills & Qualifications

1. Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related field.

2. 4-5 years of hands-on experience as an AI/ML Engineer, Data Scientist, or similar role.

3. Very strong, demonstrable expertise in Databricks, including Spark (PySpark), Delta Lake, MLflow, and Databricks Workflows.

4. Proficiency in Python and ML libraries/frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.

5. Strong understanding of machine learning algorithms, deep learning, and statistical modeling techniques.