Python ML /GenAI

Python ML /GenAI

Transformers, LangChain, Vector Databases, MLOps, Model Monitoring, MACHINE LEARNING, PYTHON, NLP, PYTORCH Preferred Qualifications: • Experience building end-to-end NLP solutions (tokenization, embeddings, vector search, evaluation) for production or near-production use cases. • Familiarity with modern GenAI patterns such as retrieval-augmented generation (RAG), prompt engineering, and response quality evaluation. • Experience with ML/GenAI experimentation frameworks, reproducibility practices, and model governance basics. • Strong understanding of model performance tuning, error analysis, and iterative improvement cycles. • Ability to work with stakeholders to refine problem statements, define success metrics, and deliver measurable outcomes. • Prior experience contributing to scalable, maintainable analytics/ML codebases with good engineering practic Key Responsibilities: • Build, train, evaluate, and iterate Machine Learning models using Python for structured and unstructured data use cases. • Develop and optimize GenAI solutions (prompting, evaluation, and tuning approaches) aligned to business needs and responsible AI practices. • Implement NLP pipelines for text preprocessing, feature extraction/embeddings, classification, summarization, or information retrieval tasks. • Perform data exploration, cleaning, and transformation to ensure high-quality inputs for ML/GenAI workflows. • Define evaluation metrics, run experiments, analyze results, and communicate insights to technical and non-technical stakeholders. • Collaborate with cross-functional teams to translate requirements into technical designs and deliverables. • Support deployment readiness by packaging models, documenting workflows, and assisting integration with downstream systems. • Monitor model performance and data drift, and contribute to continuous improvement through retraining and refinements. Minimum Qualifications: • Education: BTECH, MTECH, MCA, MSC (or equivalent). • 3–5 years of experience applying Machine Learning using Python in real-world projects. • Strong proficiency in Python for data processing, modeling, and experimentation. • Hands-on experience with ML concepts (supervised/unsupervised learning, feature engineering, model validation). • Working knowledge of Generative AI concepts and practical implementation approaches. • Exposure to NLP techniques and text-based modeling workflows. • Ability to communicate clearly, collaborate effectively, and document solutions for reuse and maintainability.