Summary The AI Engineer role is critical in driving innovation by identifying and developing advanced AI technologies that align with the company’s strategic objectives. This position enhances data-driven decision-making capabilities and establishes best practices for AI model development and deployment. Through forward-thinking, the AI Engineer ensures seamless integration of scalable, reliable, and tailored AI solutions into existing frameworks, addressing the unique needs of the pharmaceutical industry. This role contributes significantly to advancing healthcare through technology, improving patient outcomes, and driving business success. About the Role Key Responsibilities • Analyze complex business problems, formulate integrated analytical approaches, mine data sources, and apply statistical methods and machine learning algorithms to address unmet medical needs, uncover actionable insights, and automate processes for efficiency. • Design and develop end-to-end AI/ML and Generative AI solutions, emphasizing scalability, performance, and modularity while adhering to enterprise architecture standards and best practices. • Oversee the data lifecycle, managing data acquisition, enrichment, consumption, retention, and retirement to ensure the availability of clean, accurate, and useful data. • Demonstrate high agility to work across various business domains, integrating business presentations, smart visualization tools, and contextual storytelling to effectively communicate findings to business users. • Independently manage budgets, ensure appropriate staffing, and coordinate projects within the area of responsibility. • Collaborate with globally dispersed stakeholders and cross-functional teams to solve critical business problems and deliver on high-visibility strategic initiatives. Essential Requirements Education & Qualifications • Advanced degree in Computer Science, Engineering, or a related field (PhD preferred). Experience • 8+ years of experience in AI/ML engineering (data engineering experience may be considered), including at least 2 years focused on designing and deploying Large Language Model (LLM)-based solutions. • Strong expertise in building AI/ML architectures and deploying models at scale using cloud platforms such as AWS, Google Cloud, or Azure. • Deep understanding of LLMs and their application in business contexts. • Proficiency in containerization technologies (e.g., Docker, Kubernetes) and CI/CD pipelines. • Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps tools for scalable deployment. • Expertise in API development, integration, and model deployment pipelines. • Strong problem-solving skills with a proactive, hands-on approach to challenges. • Ability to collaborate effectively in cross-functional teams and communicate technical concepts clearly. • Excellent organizational skills and attention to detail in managing complex systems.