Roles and Responsibilities

- Frame ambiguous R&D; and regulatory questions as well-posed modeling problems, selecting the appropriate method — classification, sequence modeling, information extraction, retrieval, generation, or statistical inference — and justifying the choice against alternatives.

- Build and evaluate NLP and LLM systems over large corpora of scientific and regulatory text: document parsing and layout-aware extraction, named entity and relation extraction, semantic search and reranking, retrieval-augmented generation, structured output, and multi-step agent orchestration with tool use.

- Develop predictive and statistical models over structured scientific data — analytical method and stability data, formulation and process parameters, historical submission outcomes — including feature engineering, handling of small and imbalanced datasets, uncertainty quantification, and model interpretability.

- Mine historical health authority correspondence (deficiency letters, information requests, complete response letters) to learn recurring deficiency patterns and build submission risk-scoring models.

- Design and own the evaluation discipline for every system shipped: curated golden datasets,



inter-annotator agreement, failure taxonomies, offline and online metrics, A/B and regression testing, and quantitative acceptance criteria agreed with the business.

- Build reusable modeling components — embedding and retrieval pipelines, extraction schemas, evaluation harnesses, fine-tuning and prompt-optimization workflows — on Databricks (Unity Catalog, Delta Lake, MLflow, Mosaic AI) and cloud model services (Amazon Bedrock, SageMaker).

- Operationalize models with monitoring for data drift, performance degradation, and cost, and own remediation and periodic retraining.

- Produce audit-quality technical documentation — model cards, data lineage, validation evidence — supporting Amneal's GxP AI validation framework and 21 CFR Part 11 requirements, working with QA on inspection readiness.

- Establish benefit baselines with R&D; and Regulatory owners and measure realized cycle-time, accuracy, and capacity outcomes.

- Present methods, results, limitations, and trade-offs to technical and business audiences; mentor junior scientists and engineers on modeling practice.

📌 Senior Manager, Application Development (Ahmedabad)
🏢 Amneal India
📍 Ahmedabad