AI Engineer. DDIT Dev.DSAI

Data Science and AI Group (DSAI) is responsible for overseeing the development and implementation of data science and AI initiatives within an DDIT organization for Drug Development.

We are seeking a skilled AI Engineer to join our team, the role involves the development, fine-tuning, and deployment of sophisticated language models, such as GPT, tailored to fulfill specific business requirements. Responsibilities include optimizing model performance and integrating these models into production systems.
The ideal candidate should possess a robust background in natural language processing (NLP), machine learning, and software engineering, with a strong inclination towards advancing innovation in artificial intelligence (AI).

• As an AI engineer have the opportunity to apply your deep expertise in LLM/GenAI technologies in solving real-world healthcare challenges.
• Your main responsibility will be collaborating closely with Drug Development teams to prototype, build, test, and deploy products powered by GenAI/LLM technology on a large scale.
• Accountable for deploying the generative AI solutions.
• Delivery of projects in close collaboration with platform and business teams

• Bachelor s or master s degree in computer science, Data Science, Machine Learning, or a related field.

• 5+ years of experience in NLP, machine learning, or AI development, with a focus on language models.
• Strong programming skills in Python, and experience with libraries such as TensorFlow, PyTorch, Hugging Face, or spaCy.
• Hands-on experience with deploying machine learning models into production environments (e.g., using Docker, Kubernetes, AWS, Azure).
• Familiarity with prompt engineering techniques and fine-tuning large language models.
• Knowledge of MLOps practices and tools for monitoring model performance.
• Understanding of AI ethics and compliance issues, including bias mitigation and privacy concerns.
• Customize pre-trained language models for specific use cases by fine-tuning them on domain-specific datasets.
• Experiment with hyperparameters, model architectures, and training techniques to optimize performance.
• Curate, preprocess, and augment large datasets to improve the quality and generalization of language models.
• Handle tasks related to data cleaning, labeling, and tokenization.
• Design and optimize prompts to guide the models responses for various tasks.
• Apply prompt tuning, zero-shot, and few-shot learning techniques to improve model accuracy.
• Deploy models in production environments, ensuring they meet performance and scalability requirements.
• Stay updated on industry trends, compliance, and responsible AI guidelines.

• Experience with generative AI models, such as GPT-3, GPT-4, AWS Bedrock Cludia or other LLMs.
• Familiarity with few-shot and zero-shot learning techniques
• Knowledge of cloud-based AI services and serverless architectures.
• Ability to work in an agile development environment and collaborate with cross-functional teams
• Life science / Pharmaceutical domain experience is a big plus

ai, Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Microsoft Azure, Natural Language Processing (NLP), Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling