Role Summary

Develop and deploy AI/ML models — including NLP, speech, and predictive models — that power client solutions within a Delivery POD, working closely with Data Engineers and MLOps to bring models into production.

Key Responsibilities

• Design, train, and evaluate ML models addressing the POD's client use cases
• Collaborate with Data Engineers to build feature pipelines and ensure data quality for models
• Partner with MLOps to productionize and monitor models in live client environments
• Continuously improve model performance based on production feedback and business metrics
• Document model design decisions, assumptions, and limitations for governance and audit purposes
• Stay current with advances in AI/ML relevant to the POD's domain (NLP, speech, forecasting, etc.)

Must-Have — Core Technical Skills

• Strong experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Proficiency in Python and experience with model development lifecycle end-to-end
• Experience with NLP, speech, or LLM-based systems (as relevant to the POD)
• Understanding of model evaluation, validation, and performance tuning
• Familiarity with MLOps practices for deploying and monitoring models in production

Good to Have

• Experience building ML models for financial use cases (credit risk, fraud detection, forecasting, document intelligence)
• Understanding of model explainability and governance requirements in regulated environments
• Awareness of bias, fairness, and compliance considerations for financial decisioning models
• Experience with LLMs, RAG systems, or conversational AI
• Experience building models for Indian languages or speech recognition

Qualifications

B.Tech/M.Tech in Computer Science, Data Science, or related field.