Key Responsibilities:

• Understand complex and critical business problems, formulates integrated analytical approach to mine data sources, 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, focusing 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 from data acquisition or creation through enrichment, consumption, retention, and retirement, enabling the availability of useful, clean, and accurate data throughout its useful lifecycle.
• High agility to be able to work across various business domains. High agility to be able to 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.

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 could be appropriate depending on experience), 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 and a proactive, hands-on approach to challenges.
• Ability to work effectively in cross-functional teams and communicate technical concepts clearly.
• Excellent organizational skills and attention to detail in managing complex systems.