Sr. Principal Software and AI Engineer

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Description

Accountabilities - Lead AI application development: Architect, develop, and deploy production-ready AI-powered and agentic applications that support clinical protocol generation, study design, and clinical trial operations. - Define technical strategy and architecture: Establish scalable and reliable architectures for multi-agent orchestration, model integration, tool connectivity, and the complete LLMOps lifecycle. - Drive end-to-end product delivery: Take ownership of technical direction, project execution, priorities, scope, timelines, and software quality from initial concept through production deployment. - Establish AI engineering standards: Define and implement best practices for AI-native development, testing, evaluation, observability, continuous monitoring, and CI/CD within a rigorous software development lifecycle. - Advance AI capabilities: Evaluate emerging foundation models, open-weight models, agent frameworks, and development tools to make informed decisions about technology selection, model sourcing, and build-versus-buy strategies. - Develop enterprise knowledge systems: Design and maintain knowledge architectures, including ontologies, knowledge graphs, and retrieval systems that make complex scientific information structured, governed, and accessible to AI applications. - Ensure AI reliability and compliance: Implement evaluation frameworks combining expert benchmarks, rule-based validation, human review, and model-based assessments. Integrate data privacy

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