Senior Manager, Application Development

Overview
Amneal Pharmaceuticalsamneal.comPosted today
Ahmedabad, Gujarat, INOn-site
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Seniority level
Senior
Employment type
Full time
Specialization
Backend engineering

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.

Requirements

  • MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, Computational Linguistics, or a related quantitative field; PhD preferred (exception, Bachelors from Tier-1 colleges).
  • 5+ years building and deploying machine learning and NLP models in production, with demonstrated ownership from problem framing through monitoring.
  • Deep applied knowledge of modern ML and NLP: transformer architectures, embeddings, fine-tuning and parameter-efficient methods, retrieval-augmented generation, structured extraction, and LLM/agent evaluation.
  • Strong statistical foundations: experimental design, hypothesis testing, uncertainty quantification, and modeling with small, noisy, or imbalanced datasets.
  • Expert-level Python and the scientific ML stack (PyTorch or similar, Hugging Face, scikit-learn, pandas/PySpark); strong SQL.
  • Hands-on experience building agentic systems: multi-step orchestration, tool and function calling, and human-in-the-loop evaluation.
  • Track record of designing rigorous evaluation frameworks and defending model quality with quantitative evidence.
  • Ability to work within documented, controlled processes and produce audit-quality technical documentation.
  • Document intelligence over long, technical, or scientific documents (OCR, layout parsing, table and figure extraction from legacy PDFs).
  • Predictive modeling over chemistry, analytical, or process data; cheminformatics or chemometrics toolchains.
  • Experience in a GxP-regulated or other validated environment; familiarity with 21 CFR Part 11, CSV, or GAMP 5 and with AI model validation or governance.
  • Exposure to pharmaceutical regulatory content — ANDA/NDA/DMF, eCTD structure, SPL labeling, health authority correspondence. Not required; domain is taught on the job.
  • Databricks (Unity Catalog, Delta Lake, MLflow, Mosaic AI) and Amazon Bedrock or SageMaker.
  • Publications, open-source contributions, or competition results demonstrating applied ML depth.

Key skills

  • Natural Language Processing
  • LLM
  • Databricks
  • Unity
  • QA
  • Machine Learning
  • Statistics
  • Python
  • PyTorch
  • scikit-learn
  • Pandas
  • Apache Spark
  • SQL