Job Description

• Fine-tune, optimize, and retrain ML/AI models, including LLMs and enterprise ML models.
• Build and maintain evaluation pipelines to test accuracy, robustness, fairness, and efficiency.
• Automate ML workflows and lifecycle management using MLflow and MLOps practices.
• 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.
• Strong background in machine learning engineering and MLOps practices.
• Proficiency in Python with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn.
• Familiarity with evaluation frameworks (DeepEval, RAGAS, custom pipelines).
• Good SQL skills and ability to work with structured/unstructured datasets.
• Exposure to Spark/Databricks for data processing (basic familiarity, not full pipeline ownership).
• Familiarity with LangChain/LangGraph evaluation and testing tools.
• Knowledge of bias, fairness, and explainability tools (SHAP, LIME, InterpretML).
• Understanding of deep learning and neural network architectures