Head of Applied AI

Bask Health

. Lead the technical direction and development of applied AI capabilities across Bask Health's platform .

Posted 11/12/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the development and integration of AI capabilities, focusing on large language models, scalable systems, and production standards. Proven ability to mentor engineering teams while ensuring technical quality, reliability, and compliance in AI applications.

Highest-signal resume keywords

AI Engineering LeadershipLarge Language Model Application DevelopmentPython ProgrammingScalable Backend Service DesignAI System Performance Evaluation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringAI Application DevelopmentPrompt EngineeringModel EvaluationDistributed Application ArchitectureAPI IntegrationObservability and MonitoringData PrivacyTechnical Design ReviewAI Workflow Orchestration

Soft Skills

Technical LeadershipEngineering MentorshipAnalytical Problem-SolvingCross-Functional CollaborationCommunication of Technical Concepts

Tools & Technologies

Cloud InfrastructureModel-Serving InfrastructureAPIsMonitoring ToolsIncident Investigation Tools

Industry Keywords

Applied AIPatient ExperienceAutomation ToolsTechnical StandardsProduction Deployment Practices

Tech Stack

Tools & technologies

CloudPythonTypeScript

About the role

Key responsibilities & impact

Lead the technical direction and development of applied AI capabilities across Bask Health's platform
Define architecture and engineering standards for production AI applications
Guide systems powered by large language models and machine learning
Identify high-impact AI opportunities with leadership, product managers, and engineers
Translate product and business requirements into practical AI engineering initiatives
Lead merchant-facing AI features, intelligent assistants, and automation tools
Guide AI applications supporting patient experiences and internal operational workflows
Design scalable systems integrating AI models into existing software applications
Evaluate model providers, APIs, frameworks, and infrastructure for production use
Establish practices for prompt management, model evaluation, and output validation
Measure AI accuracy, usefulness, latency, cost, and reliability
Guide retrieval-augmented generation, tool calling, and agentic workflows
Establish safeguards and human-review mechanisms for sensitive workflows
Ensure privacy, security, and access controls for AI systems
Integrate AI services into production architecture with backend and platform engineers
Define monitoring, logging, troubleshooting, and observability standards
Improve model performance and reduce unnecessary inference costs
Investigate AI system failures, unreliable outputs, and integration challenges
Contribute hands-on code, prototypes, and architectural improvements
Review technical designs and guide implementation decisions
Mentor engineers working on AI and machine learning systems
Establish reusable AI infrastructure, services, and development patterns
Evaluate AI feature performance and user feedback with product teams
Prioritize investments improving AI reliability and scalability
Guide emerging AI technology experimentation while maintaining production standards
Support technical planning, project execution, and cross-functional coordination
Build a culture of practical experimentation, technical ownership, and continuous improvement
Own the technical quality and maintainability of Bask's applied AI systems

Requirements

What you’ll need

8+ years of professional experience in software engineering, machine learning engineering, AI engineering, or a comparable technical discipline
3+ years of experience leading engineering teams, major technical initiatives, or complex AI projects
Demonstrated experience building and deploying AI-powered applications in production
Strong understanding of large language models and practical LLM application development
Hands-on experience integrating AI models through APIs or model-serving infrastructure
Strong programming skills in Python, TypeScript, or comparable production engineering languages
Experience designing scalable backend services and distributed application architectures
Strong understanding of retrieval-augmented generation, tool calling, and AI workflow orchestration
Experience evaluating model outputs and measuring AI system performance
Understanding of prompt engineering, structured outputs, and output validation
Experience designing systems that balance model quality, latency, reliability, and cost
Familiarity with cloud infrastructure and production deployment practices
Strong understanding of software architecture, APIs, and system integrations
Experience establishing engineering standards and reviewing complex technical designs
Familiarity with observability, monitoring, and incident investigation for production services
Understanding of application security, data privacy, and responsible AI implementation
Experience designing fallback behavior and safeguards for unreliable model outputs
Ability to evaluate new AI technologies and make practical technical recommendations
Experience collaborating with product managers and business stakeholders
Strong technical leadership and engineering mentorship skills
Ability to communicate complex technical concepts to both technical and nontechnical audiences
Strong analytical and problem-solving abilities
Experience prioritizing technical initiatives and balancing competing engineering requirements
Willingness to remain hands-on with architecture, prototyping, and technical problem-solving
Comfort leading initiatives in a fast-moving, remote engineering environment