AI Engineer

Opportunity

Get Well (GW RhythmX) is seeking a highly experienced and innovative Staff AI Engineer to lead the architecture, development, and optimization of cutting-edge AI solutions across the organization’s healthcare platform. This role requires deep technical expertise in large language models (LLMs), multimodal AI systems, agentic frameworks, and voice technologies—including STT (speech-to-text) and TTS (text-to-speech).

As a Staff-level technical leader, you will guide the AI engineering lifecycle from conceptualization to deployment at scale while serving as a key cross-functional partner to product, engineering, and clinical teams. This is a high-impact, hands-on role for a forward-thinking AI expert with a strong understanding of emerging agentic systems and best practices in safety, observability, and machine learning operations in regulated environments.

Responsibilities

Healthcare-Focused AI System Design & Development

• Architect and develop production-ready AI models trained on real-world clinical datasets sourced from hospital systems, EHRs, and patient engagement platforms.
• Partner with clinical informatics and product teams to derive insights from structured and unstructured health data, including FHIR, HL7, CCDA, and EHR notes.
• Design and train speech-to-text (STT) and text-to-speech (TTS) models to power voice-enabled AI applications and virtual assistants in a healthcare setting.
• Integrate and optimize agentic systems using frameworks such as LangChain, LangGraph, or CrewAI for autonomous decision-making and workflow automation.
• Drive the end-to-end development lifecycle—from data prep and model training to evaluation, deployment, and monitoring—ensuring responsiveness and efficiency in high-impact healthcare settings.
• Evaluate and incorporate emerging AI technologies and architectural tools to improve intelligence, personalization, and user experience.

Infrastructure Optimization & MLOps

• Lead the model lifecycle from ingestion and preprocessing of healthcare datasets (e.g., EHR records, patient surveys, clinical measurements) to training, evaluation, and deployment into hospital IT ecosystems.
• Lead the design and optimization of cloud-based AI infrastructure, focusing on scalability, performance, observability, and cost-efficiency (Azure preferred).
• Establish and maintain scalable CI/CD pipelines, GPU-optimized runtimes, and real-time or batch inference systems in Azure healthcare-compliant environments.
• Ensure reliability, production-grade observability, and rollback safeguards using tools like Langfuse, Prometheus, Grafana, and other internal tools.