Develop the AI evaluation modules under the large language model operations (LLMOps), which includes the components of LLM observability, benchmark datasets, standardization of metrics, evaluation of complex LLM workflow, knowledge graph, etc
Implement strategies of AI safety for adversarial detection AI, comprehensive model evaluation, and error analysis, incorporating human feedback for continuous AI model improvement
Maintain and continuously improve existing data models and AI Engine pipelines, including monitoring performance, resolving production issues, optimizing for scalability, and ensuring robust dataflows across the AI/ML and application stack.
Apply LLM observability tools, such as LangFuse, Arize Phoenix, Braintrust, etc, for LLM traces, latency and cost awareness, evaluation, and prompt management/deployment.
Develop data pipelines in AI engine to support a generic data store and data representation, including a meta-data layer for clinical semantics and a vector database for downstream applications
Develop generative and predictive AI modules and necessary data transformation modules in AI engine alongside agentic AI systems
Support agentic AI systems development using LangGraph and/or other agentic AI frameworks for the use cases such as AI assistants in both internal applications (e.g. SafeRead) and external ones (e.g. SmarterNotes)
Collaborate with clinical domain experts and product management teams to translate clinical guidelines into AI modules, ensuring that the developed products align with clinical needs and user requirements.
Stay updated with the latest AI advancements in the generative AI era, integrating new knowledge into practical applications and contributing to the AI community through professional engagements.
Requirements
Bachelor, Master's or Ph.D. in Computer Science, Data Science, Physics, Mathematics, Engineering, Informatics, or a related field
3+ years of experience in data analytics and statistical/ML/NLP modeling, with a focus on healthcare applications
Proven experience with cloud infrastructure and services, particularly in the deployment of AI/ML models
Expertise in programming with Python and familiarity with AI/ML tools and frameworks
Familiarity with vibe coding tools, such as Claude Code, Codex, Cursor, Replit, etc
Demonstrated ability in the development and management of large-scale AI systems
Proficiency in cloud services (AWS, Azure) and familiarity with EKS clusters, Docker, Kubernetes, and MLOps practices
Strong background in generative AI, NLP, machine learning, and data science with a passion for innovation in healthcare AI
Exceptional problem-solving abilities and a commitment to quality and ethical AI practices
Excellent communication skills, capable of translating complex technical concepts to diverse stakeholders
Leadership and project management skills, with an ability to drive projects to completion in a fast-paced environment
Benefits
Shared premiums: medical, dental and vision, plus access to FSA and HSA
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