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

• Advanced degree in Computer Science, Engineering, or a related field

• 5+ 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.