Asks for

The ad says“Integrate and optimize agentic systems using frameworks such as LangChain, LangGraph, or CrewAI for autonomous decision-making and workflow automation.”
The ad says“Familiarity with healthcare data standards (FHIR, HL7, SNOMED, ICD-10) and clinical integration best practices”
The ad says“Ensure reliability, production-grade observability, and rollback safeguards using tools like Langfuse, Prometheus, Grafana, and other internal tools.”
The ad says“Familiarity with healthcare data standards (FHIR, HL7, SNOMED, ICD-10) and clinical integration best practices”
The ad says“Proficient in AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and model serving stacks.”
The ad says“Familiarity with healthcare data standards (FHIR, HL7, SNOMED, ICD-10) and clinical integration best practices”
The ad says“Integrate and optimize agentic systems using frameworks such as LangChain, LangGraph, or CrewAI for autonomous decision-making and workflow automation.”
The ad says“Ensure reliability, production-grade observability, and rollback safeguards using tools like Langfuse, Prometheus, Grafana, and other internal tools.”
The ad says“Integrate and optimize agentic systems using frameworks such as LangChain, LangGraph, or CrewAI for autonomous decision-making and workflow automation.”
The ad says“LLMs (e.g., OpenAI, LLaMA, Claude) and transformer-based NLP models”
The ad says“Multimodal learning architectures integrating text, image, and structured healthcare data”
The ad says“Ensure reliability, production-grade observability, and rollback safeguards using tools like Langfuse, Prometheus, Grafana, and other internal tools.”
The ad says“Advanced programming in Python (required); familiarity with C++ is a plus.”
The ad says“Proficient in AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and model serving stacks.”
The ad says“Familiarity with healthcare data standards (FHIR, HL7, SNOMED, ICD-10) and clinical integration best practices”
The ad says“Proficient in AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and model serving stacks.”
The ad says“LLMs (e.g., OpenAI, LLaMA, Claude) and transformer-based NLP models”

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.

Monitoring, Observability & Reliability

Set up and manage observability tools and frameworks such as Langfuse, Prometheus, Grafana, or equivalent to monitor operational health of AI models and agentic workflows.
Establish proactive monitoring for model performance, agent behavior, anomaly detection, and feedback loop management.
Rapidly diagnose and address system bottlenecks, drift, or failure points in production environments.

Healthcare Compliance & Responsible AI

Ensure all AI solutions adhere to HIPAA, GDPR, and internal privacy and data security standards.
Design and enforce ethical AI principles, focusing on bias mitigation, explainability, reproducibility, and accountability.
Oversee secure handling and governance of sensitive data, including ePHI and PHI, in compliance with Federal, State, and local regulations.

Cross-Functional Collaboration & Technical Leadership

Act as a principal technical liaison between AI engineering, product, design, and clinical stakeholders.
Translate complex technical architectures into product-aligned features and user-centric outcomes.
Collaborate closely with clinical experts to ensure AI solutions address high-impact, evidence-based healthcare needs.
Mentor and elevate junior engineers through architecture reviews, hands-on pairing, and code quality leadership.
Drive a culture of innovation, excellence, and learning across AI engineering and data science teams.

Qualifications

Education & Experience

Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field.
8–10+ years of hands-on experience in AI/ML development, with 3+ years in technical leadership or Staff/Principal-level roles.
Proven track record of delivering production-grade AI systems in healthcare industries.

Technical Expertise

Deep expertise in:
- LLMs (e.g., OpenAI, LLaMA, Claude) and transformer-based NLP models
- Multimodal learning architectures integrating text, image, and structured healthcare data
- Fine-tune SLMs / LLMs and develop ML models for healthcare-specific use cases.
- Agentic AI systems using frameworks like CrewAI, LangChain, and LangGraph
- STT/TTS models (e.g., Whisper, Tacotron, FastSpeech, DeepSpeech)
Advanced programming in Python (required); familiarity with C++ is a plus.
Proficient in AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and model serving stacks.
Hands-on experience with MLOps frameworks, infrastructure-as-code, container orchestration, and model registries.
Familiarity with healthcare data standards (FHIR, HL7, SNOMED, ICD-10) and clinical integration best practices
Awareness of cutting-edge trends in Agentic Systems, Multimodal Context Processing (MCP), A2A (Agent-to-Agent) protocols, and healthcare-centric AI safety practices.

Professional Attributes

Strong analytical and problem-solving abilities with a bias for action.
Excellent communicator—able to translate complex AI systems to diverse stakeholders.
Proven ability to work in fast-paced, cross-functional, and agile product environments.
Committed to high standards of privacy, compliance, and ethical AI.
Demonstrated experience mentoring engineers and influencing platform and product direction through thought leadership.
Adaptability to rapid technological shifts and emerging AI frameworks.

About GW RhythmX

GW RhythmX is revolutionizing healthcare through connected, AI-native intelligence that unites clinical insight, patient engagement, and system-wide care orchestration. The company combines market-leading AI precision care technology with extensive trusted patient engagement leadership to help health systems deliver the right care, at the right time, through the right clinician and channel. Its solutions are deployed across more than 150 health systems, touching more than 85M patients including 8M U.S. military veterans. The company's award-winning solutions were recognized again in 2024 by KLAS Research, Fierce Healthcare, and AVIA Marketplace. A SymphonyAI Group company, GW RhythmX leverages various firm assets, including $1B+ in R&D investment, longitudinal data related to 300 million patients, 4.4 billion total annual claims, and 1.8 million healthcare professionals at more than 3,000 facilities globally.

GW RhythmX is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age or veteran status.