Python ML /GenAI

Transformers, LangChain, Vector Databases, MLOps, Model Monitoring, MACHINE LEARNING, PYTHON, NLP, PYTORCH

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. 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