Intern - US&I AI Engineering

Role Overview: You will play a crucial role in identifying and incubating cutting-edge AI technologies that align with the strategic goals of the company. Your responsibilities will include enhancing capabilities in data-driven decision-making, defining and promoting best practices in AI model development and deployment, and ensuring seamless integration of innovative AI solutions into existing frameworks tailored to meet the unique demands of the pharmaceutical industry. Your contribution will advance healthcare through technology, ultimately improving patient outcomes and driving business success. Key Responsibilities: - Understand complex and critical business problems, formulate integrated analytical approaches, employ statistical methods and machine learning algorithms to contribute to solving unmet medical needs, discover actionable insights, and automate processes for reducing effort and time for repeated use. - Architect and develop end-to-end AI/ML and Gen AI solutions with a focus on scalability, performance, and modularity while ensuring alignment and best practices with enterprise architecture standards. - Manage the implementation and adherence to the overall data lifecycle of enterprise data, enabling the availability of useful, clean, and accurate data throughout its useful lifecycle. - Work across various business domains, integrate business presentations, smart visualization tools, and contextual storytelling to translate findings back to business users with a clear impact. - Independently manage budget, ensuring appropriate staffing and coordinating projects within the area. - Collaborate with globally dispersed internal stakeholders and cross-functional teams to solve critical business problems and deliver successfully on high visibility strategic initiatives. Qualifications Required: - Advanced degree in Computer Science, Engineering, or a related field (PhD preferred). - 5+ years of experience in AI/ML engineering with at least 2 years focusing on designing and deploying LLM-based solutions. - Strong proficiency in building AI/ML architectures and deploying models at scale with experience in cloud computing platforms such as AWS, Google Cloud, or Azure. - Deep knowledge of LLMs and experience in applying them in business contexts. - Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD pipelines. - Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps tools for scalable deployment. - Experience with API development, integration, and model deployment pipelines. - Strong problem-solving skills, proactive hands-on approach to challenges, and ability to work effectively in cross-functional teams. - Excellent organizational skills and attention to detail in managing complex systems.,