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Specialized MLOps/cloud role with mid-level experience at a strong employer, moderate applicant density.
Requires cloud MLOps skills plus regulatory and healthcare compliance knowledge, reducing cross-industry transferability.
Multiple explicit years, mandatory AWS/MLOps skills, and regulatory compliance increase filtering stringency.
Design, build, and govern secure, scalable, multi-tenant enterprise AI platforms on AWS supporting AI/ML, Generative AI, LLMOps, Agentic AI, and MLOps.
Develop and enable Generative AI and Agentic AI solutions using Amazon Bedrock, AWS Agent Core, and other AWS AI/ML services like SageMaker.
Ensure compliance with Responsible AI principles and regulations (HIPAA, GDPR, GxP, FDA, EU AI Act), and enforce AWS security controls, governance, and AI safety mechanisms.
3+ years of Python development and platform engineering experience.
2+ years of hands-on AWS cloud engineering experience with strong expertise in AWS AI/ML services such as SageMaker, Bedrock, Agent Core.
1+ years of experience in AI/ML, MLOps, data platforms, or Generative AI infrastructure.
Proficiency in Python and AI orchestration frameworks (e.g., LangChain, LlamaIndex, DSPy); knowledge of AI governance, regulatory compliance, and AWS security controls.
Experienced in operating and scaling production-grade AI/ML platforms within highly regulated industries, especially healthcare/pharma.
Technically skilled in architecting end-to-end AI solutions on AWS, incorporating security, scalability, and governance considerations.
Familiar with implementing AI platform standards, onboarding frameworks, and fostering adoption of AI/ML technologies in enterprise settings.