Senior AI Engineer

Title: Senior AI Engineer
 

Reporting to: SVP, Data & AI
 

Location: USA (Remote)


Opportunity:

Get Well is seeking a highly skilled and innovative Senior AI Engineer to join our growing team of AI experts, focused on building and operationalizing advanced AI solutions that drive business value, operational efficiency, and personalized patient engagement. This role is ideal for an experienced technologist with 5+ years of hands-on experience in LLMs, ML, and related technologies, especially in training, customizing, and deploying models for real-world use cases.
The successful candidate will work through all phases of the AI development life cycle, from data strategy and model development to production deployment, monitoring, and continuous optimization. You will collaborate closely with product, engineering, and clinical teams to ensure integration of scalable, secure, and high-impact AI systems in our business processes.
This position reports directly to the SVP, Data & AI, collaborating with cross-functional teams to implement AI solutions that enhance precision care, patient engagement and operational efficiency.
 

Responsibilities:

AI Model Development and Customization

• Lead the design, development, and fine-tuning of large language models (LLMs) tailored for business-critical healthcare applications
• Apply domain adaptation techniques to foundation models for operational use cases
• Build multimodal AI systems that integrate structured and unstructured data sources 
• Optimize model performance, accuracy, and efficiency for production environments

Technical Implementation

• Write clean, maintainable code for AI model training, evaluation, and inference at scale
• Build and manage data pipelines and MLOps frameworks for continuous learning 
• Implement best practices for model versioning, experiment tracking, and reproducibility
• Integrate AI solutions with existing product architectures and infrastructure

Data Engineering and Management

• Prepare and process high-quality healthcare and operational data for model training and validation
• Apply privacy-preserving techniques and comply with healthcare data regulations (e.g., HIPAA)
• Implement synthetic data generation or augmentation strategies to enhance training datasets
• Address data quality issues such as class imbalance, sparsity, and noise in sensitive domains

Evaluation and Bias Assessment

• Design robust evaluation protocols and business-focused KPIs to assess AI model performance
• Monitor and improve fairness, transparency, and bias mitigation across AI solutions
• Conduct rigorous A/B testing, scenario simulations, and error analysis to ensure system reliability
• Track AI outcomes to support measurable gains in efficiency, accuracy, and user experience

Collaborative Development

• Partner with product and operations leaders to define AI requirements aligned with business goals
• Collaborate with domain experts to ensure model outputs are actionable and trustworthy
• Participate in agile development processes, including sprint planning and reviews
• Document solution architecture, modeling assumptions, and implementation processes

Continuous Learning and Innovation

• Stay abreast of industry trends in LLMs, MLOps, and applied AI for business transformation
• Rapidly prototype and test emerging AI capabilities with practical business applications
• Contribute to internal tooling, frameworks, and reusable components for scalable AI development
• Proactively identify efficiency opportunities across business workflows through AI automation
   

Requirements:

• Master’s degree in Computer Science, Artificial Intelligence, ML, or related technical field
• 5+ years of hands-on experience with:
  • Training and fine-tuning LLMs
  • Implementing multimodal AI solutions
  • Working through the complete AI development lifecycle
  • Developing AI solutions for complex domain use cases
• Technical proficiency in:
  • Python programming and ML frameworks (PyTorch, TensorFlow, or equivalent)
  • Fine-tuning techniques for LLMs (prompt engineering, PEFT, LoRA, etc.)
  • Natural Language Processing (NLP) and Understanding (NLU) techniques
  • Cloud computing and ML operatio