AINQA Health QDM

Saravanampatti Coimbatore

Design, develop, and test prompts to optimize performance of large language models (LLMs) across healthcare use cases. Collaborate closely with AI researchers, product managers, and healthcare domain experts to define prompt requirements that align with clinical workflows. Continuously refine prompts based on testing, real-world performance, and end-user feedback to improve model accuracy, contextual relevance, and user satisfaction. Develop libraries of reusable, modular prompts for common healthcare tasks like summarizing clinical notes, generating reports, and providing diagnostic suggestions. Analyze the behavior of AI outputs, identify edge cases, and troubleshoot inaccuracies to improve system reliability. Ensure prompts adhere to industry-specific compliance standards, such as HIPAA, and support secure handling of sensitive healthcare data. Document prompt designs, iterations, and testing outcomes to build a robust knowledge base for continuous improvement. Stay current with advancements in LLMs, NLP, and AI techniques, particularly as they apply to healthcare challenges.Requirements: Bachelors or masters degree in computer science, Artificial Intelligence, Linguistics, Healthcare Informatics, or a related field. Relevant experience may substitute for formal education. 2+ years of experience working with NLP, large language models (e.g., GPT, Claude, etc.), or AI systems. Demonstrated experience in prompt design, fine-tuning, or optimization of AI models. Strong understanding of healthcare workflows, terminologies (e.g., ICD, CPT codes), and clinical documentation processes. Proficiency in Python, APIs for LLMs, or related tools to test and validate prompts. Ability to analyze AI performance metrics and provide structured feedback for model improvement. Excellent problem-solving and analytical skills, with a keen eye for detail. Exceptional written and verbal communication skills to articulate complex ideas to technical and

Qualifications: 5-7 years of professional experience in machine learning, data science, or related roles. Should have good exposure and understanding in time series Modelling using ARIMA, ARIMAX Exposure into how to handle underfitting and overfitting. Should be capable of applying techniques which helps to generalize Models. Regularization techniques LASSO, RIDGE & ELASTIC NET and when to apply them. Good exposure in Unsupervised machine learning like clustering, dimensionality reduction, Outlier detection Ability to understand how Models are optimized using various techniques including Gradient Descent approach. Good understanding of deep learning algorithms CNN, RNN, LSTM and how to control overfitting in such cases. Good hands on in data engineering to process huge scale of data using Big Data (Spark/Hive) Good coding practices to write production ready code for creating data pipeline for Models to consume. Very good hands on in python (Pandas/Numpy/Scikit-Learn/NLTK/spaCy/Matplotlib) Able to apply the right level of ML techniques for the given problem statement. Ability to access information contained in data and engineer appropriate features. Familiar with Python language and various platforms for hosting ML models Expert in model training, tuning and validation. Expert in statistical techniques, deep learning methodologies, GenAI, alternate techniques such as Bayesian etc. Exposure to big data and related models Ability to articulate model choice and convert outcome for business decision making. Expert in Model Development Lifecycle from sourcing to model monitoring Ability to create code that is highly performance in the given platform. Ability to map model and business use case to the appropriate platform and tools needed. Understanding of technical and machine learning governance Ability to validate and articulate model choices with relevant metrics (precision, recall, confusion matrix, RMSE,

Responsibilities: Research, design, and develop LLM-based models and algorithms to solve real-world challenges. Collaborate with cross-functional teams including data scientists, software engineers, and domain experts to understand requirements and develop innovative solutions. Explore and implement state-of-the-art techniques in LLM, NLP, and machine learning to improve model performance and efficiency. Perform data preprocessing, feature engineering, and model evaluation to ensure high-quality results. Optimize LLM models for scalability, speed, and resource efficiency to meet production requirements. Stay updated with the latest advancements in LLM, NLP, and machine learning research and integrate relevant findings into our projects. Contribute to the development of best practices, guidelines, and documentation for LLM development and deployment. Develop and fine-tune Language Models (LLMs) to improve performance and accuracy. Collaborate with research teams to understand project requirements and develop custom LLM solutions. Implement and optimize LLM architectures for specific use cases and applications. Stay up to date with the latest advancements in LLM research and technology. Write clean, efficient, and maintainable code for LLM development projects. Conduct testing and debugging to ensure the quality of LLM implementations. Document code, processes, and best practices for LLM development.

Work with senior developers to build, train, and optimize machine learning models for various applications. Gather and preprocess data from different sources to ensure high-quality inputs for machine learning models. Conduct rigorous testing of AI models, including performance evaluation and troubleshooting. Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to deliver AI solutions. Stay up to date with the latest AI trends and technologies and contribute to research initiatives aimed at improving our AI capabilities. Maintain clear and comprehensive documentation of AI models, experiments, and results.