AI/ML Engineer

As a highly skilled AI/ML Engineer, your role will involve leading the development, scaling, and productionization of an advanced AI Agent ecosystem for a maternal-care platform. You will be responsible for orchestrating multi-agent LLM systems and developing machine learning models to analyze complex clinical data. Your primary focus will be on overseeing and maturing three critical intelligent agents: Agent 1 for production billing reconciliation and payer eligibility, Agent 2 for navigation automation, and Agent 3 for clinical pattern recognition. Key Responsibilities: - Agent Orchestration: Design, build, and optimize multi-agent workflows using TypeScript/Node.js to interact with enterprise LLM APIs. - ML Pattern Recognition: Develop and train specialized Python-based machine learning pipelines to detect anomalies, trends, and risk indicators within a large clinical escalation corpus. - Agent Lifecycle Management: Maintain and improve Agent 1 for cross-reconciliation across various domains, advance Agent 2 through deployment phases, and mature Agent 3 from build to production readiness. - Data Pipeline Integration: Collaborate with the data engineering team to process structured and unstructured data via Snowflake (Snowpark / Python APIs) while ensuring compliance with HIPAA standards for handling Protected Health Information (PHI). - System Performance & Evaluation: Establish evaluation frameworks for LLM outputs to ensure clinical safety, accuracy, and mitigate hallucinations in triage recommendation queues. Required Technical Skills & Qualifications: - Languages: Advanced proficiency in Python for ML data science workloads and TypeScript/Node.js for backend orchestration and API integration. - AI/LLM Frameworks: Strong experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or LangGraph, and commercial/open-source LLM APIs. - Machine Learning NLP: Deep understanding of Natural Language Processing (NLP), text embedding generation, vector databases, and pattern-recognition techniques applied to unstructured text datasets. - Data Stack: Hands-on experience with Snowflake and Snowpark using Python APIs. - Healthcare Domain (Highly Preferred): Familiarity with US healthcare compliance, HIPAA data privacy requirements, and navigating clinical nomenclature. As a highly skilled AI/ML Engineer, your role will involve leading the development, scaling, and productionization of an advanced AI Agent ecosystem for a maternal-care platform. You will be responsible for orchestrating multi-agent LLM systems and developing machine learning models to analyze complex clinical data. Your primary focus will be on overseeing and maturing three critical intelligent agents: Agent 1 for production billing reconciliation and payer eligibility, Agent 2 for navigation automation, and Agent 3 for clinical pattern recognition. Key Responsibilities: - Agent Orchestration: Design, build, and optimize multi-agent workflows using TypeScript/Node.js to interact with enterprise LLM APIs. - ML Pattern Recognition: Develop and train specialized Python-based machine learning pipelines to detect anomalies, trends, and risk indicators within a large clinical escalation corpus. - Agent Lifecycle Management: Maintain and improve Agent 1 for cross-reconciliation across various domains, advance Agent 2 through deployment phases, and mature Agent 3 from build to production readiness. - Data Pipeline Integration: Collaborate with the data engineering team to process structured and unstructured data via Snowflake (Snowpark / Python APIs) while ensuring compliance with HIPAA standards for handling Protected Health Information (PHI). - System Performance & Evaluation: Establish evaluation frameworks for LLM outputs to ensure clinical safety, accuracy, and mitigate hallucinations in triage recommendation queues. Required Technical Skills & Qualifications: - Languages: Advanced proficiency in Python for ML data science workloads and TypeScript/Node.js for backend orchestration and API integration. - AI/LLM Frameworks: Strong experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or LangGraph, and commercial/open-source LLM APIs. - Machine Learning NLP: Deep understanding of Natural Language Processing (NLP), text embedding generation, vector databases, and pattern-recognition techniques applied to unstructured text datasets. - Data Stack: Hands-on experience with Snowflake and Snowpark using Python APIs. - Healthcare Domain (Highly Preferred): Familiarity with US healthcare compliance, HIPAA data privacy requirements, and navigating clinical nomenclature.