Opportunity
Get Well 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.