LLM Engineer - Pharma Life Sciences - Senior Manager

The Opportunity Join our Acceleration Center India and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day. As an LLM Engineer - Pharma Life Sciences - Senior Manager, you will play a pivotal role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Within our Data and Analytics Engineering practice, you will focus on leveraging advanced analytics and statistical techniques to extract insights from large datasets and drive data-driven decision making. As a Senior Manager, you will leverage your influence, skills, and network to deliver quality results. You motivate and coach others, coming together to solve complex problems. As you increase in autonomy, you apply sound judgment, recognizing when to take action and when to escalate. In this role at PwC Acceleration Center India, you will craft and convey clear, impactful messages that tell a holistic story. You will apply systems thinking to identify underlying problems and opportunities, validate outcomes with clients, and act on client feedback. Directing the team through complexity, you will demonstrate composure through ambiguous, challenging, and uncertain situations. Your ability to develop and sustain high-performing, diverse, and inclusive teams, and your commitment to excellence, contributes to the success of our firm. Responsibilities - Leading the design and development of advanced data solutions to transform raw data into actionable insights - Driving data-driven decision-making through exploratory and descriptive analysis and statistical modeling - Utilizing advanced analytics and statistical techniques to extract insights from large datasets - Developing and implementing data visualizations to solve complex business problems and inform strategic decisions - Managing and coaching teams to apply systems thinking and identify underlying problems and opportunities - Validating outcomes with clients and acting on feedback to enhance project success - Directing teams through ambiguous and challenging situations with composure and sound judgment - Applying advanced machine learning and artificial intelligence techniques to optimize data science workflows - Building and maintaining data pipelines and confirming data quality and integrity - Promoting the use of innovative technologies and leading practices in data science and analytics - Collaborating with stakeholders to identify and address data challenges and opportunities - Crafting and conveying clear, impactful messages that tell a holistic story through data analysis What You Must Have - At least a Bachelor's degree - At least 12 years of experience - Oral and written proficiency in English required What Sets You Apart - Preference for at least one of the following fields of study: Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering - Demonstrating advanced skills in data science algorithms and workflows - Utilizing machine learning and deep learning techniques for complex problem-solving - Applying systems thinking to identify and address underlying issues - Leading teams through ambiguity with composure and strategic questioning - Excelling in crafting and conveying impactful, holistic messages - Validating outcomes with clients and acting on feedback effectively

- End to End Infra to deploy AI model (Pharma experience) - LLM Framework, LLM Ops - Rag pipeline