Python ML /GenAI

This Data & Analytics Unit role develops machine learning models and generative AI solutions in Python for structured and unstructured data use cases. It is based in Bangalore, India.

Responsibilities

• Build, train, evaluate and refine machine learning models. Explore, clean and transform data to prepare reliable inputs for ML and GenAI workflows.
• Develop and optimize GenAI solutions using prompting, evaluation and tuning approaches aligned with business needs and responsible AI practices. Build NLP pipelines for text preprocessing, feature extraction and embeddings, classification, summarization or information retrieval.
• Define evaluation metrics, run experiments, analyze results and explain findings to technical and non-technical stakeholders. Work with cross-functional teams to turn requirements into technical designs and deliverables.
• Package models, document workflows and assist with downstream integration and deployment readiness. Monitor model performance and data drift, contributing to retraining and other improvements.

Minimum qualifications:

• A BTech, MTech, MCA, MSc or equivalent qualification; the educational requirements also list Bachelor of Engineering.
• 3–5 years of experience applying machine learning with Python in real-world projects. Strong Python proficiency for data processing, modeling and experimentation; hands-on knowledge of supervised and unsupervised learning, feature engineering and model validation.
• Working knowledge of practical generative AI approaches, exposure to NLP and text modeling, and the ability to communicate, collaborate and document maintainable solutions.

Preferred qualifications

• Experience delivering end-to-end NLP solutions involving tokenization, embeddings, vector search and evaluation in production or near-production settings.
• Familiarity with retrieval-augmented generation, prompt engineering and response-quality evaluation; ML/GenAI experimentation frameworks, reproducibility and basic model governance.
• Strength in performance tuning, error analysis and iterative improvement; experience refining problems and success metrics with stakeholders and contributing to scalable, maintainable analytics or ML codebases. Listed technical skills include Transformers, LangChain, vector databases, MLOps, model monitoring and PyTorch; preferred skills also reference Amazon ML and RAG.