Responsibilities:

Fine-tune, optimize, and retrain ML/AI models.
Build and maintain evaluation pipelines to test accuracy, robustness, fairness, and efficiency.
Automate ML workflows and lifecycle management.
Access and prepare high-quality datasets for training and evaluation.
Perform light feature engineering and data transformations needed for model optimization.
Implement monitoring and feedback loops to track model performance post-deployment.
Conduct benchmarking and A/B testing to validate model improvements.
Work with Databricks Mosaic AI and cloud ML services (Azure ML, AWS SageMaker) for scalable workloads.

Required Skills and Experience:

Experience (4 to 8 Years), Proven background in machine learning engineering and MLOps practices.
Proficiency in Python with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn.
Hands-on experience with MLflow for model tracking, deployment, and lifecycle management.
Experience fine-tuning LLMs or training traditional ML models.
Familiarity with evaluation frameworks (DeepEval, RAGAS, custom pipelines).
Strong SQL skills and ability to work with structured/unstructured datasets.
Exposure to Spark/Databricks for data processing.
Understanding of Deep Learning and Neural Network architectures.
Experience with cloud ML platforms (Azure, AWS).

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