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).

“Nice To Have” Skills and Experience:

• Familiarity with LangChain/LangGraph evaluation and testing tools.
• Experience with vector databases (Pinecone, FAISS, Weaviate, Chroma).
• Knowledge of bias, fairness, and explainability tools (SHAP, LIME, InterpretML).
• Awareness of modern ML benchmarks (HELM, MMLU) for LLM evaluation.