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, implement, and manage end-to-end AI/ML pipelines on AWS, with a strong emphasis on infrastructure, scalability, and automation using tools such as CloudFormation.
• Design and maintain CI/CD pipelines for ML model development and deployment using tools like AWS CodePipeline, or Jenkins, ensuring seamless integration from data ingestion to production inference.
• Translate complex AI/ML architectures into clean, modular, and reusable infrastructure patterns that align with enterprise cloud and DevOps standards.
• Support the monitoring and governance of the entire ML lifecycle, including data lineage, metadata tracking, and artifact management.
• Work across business domains to automate and standardize ML workflows, reduce manual effort, and increase reliability and reproducibility.
• Manage infrastructure costs, resource scaling strategies, and project timelines for ML platforms and services.
• Collaborate with data engineers and data scientists to ensure smooth deployment of data pipelines and AI/ML solutions on AWS infrastructure (S3, Lambda, Step Functions, Glue, etc.).

Essential Requirements

• Advanced degree in Computer Science, Engineering, or a related field experience, Certifications in AWS (e.g., AWS Certified DevOps Engineer or ML Specialty) are a strong plus.

• 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.