Python ML /GenAI

Responsibilities :

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).

3â€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.

Additional Responsibilities:

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

Technical and Professional Requirements:

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