About the RoleWe're looking for a Senior Applied AI Engineer, Patient Experience to design, build, and ship AI-powered capabilities that improve how patients interact with Bask Health's telehealth platform.
This is a hands-on engineering role focused on bringing large language models, intelligent automation, and machine learning into real patient-facing product experiences. You'll work on practical AI features that help patients understand platform workflows, complete onboarding, navigate treatment-related information, and interact with digital healthcare services more effectively.
You'll collaborate closely with product managers, frontend engineers, backend engineers, and clinical stakeholders to identify where AI can simplify complicated experiences without introducing unnecessary risk or friction.
The work goes beyond building prototypes or connecting an application to an LLM API. You'll be responsible for production-ready implementations, including model selection, retrieval systems, tool integrations, evaluation, monitoring, and reliable fallback behavior.
Because these systems operate in a healthcare environment, you'll need to think carefully about accuracy, privacy, sensitive information, and the boundaries between helpful AI assistance and decisions that require qualified clinical professionals.
You'll have meaningful ownership over technical decisions and will help establish reusable patterns for delivering dependable AI features across patient-facing applications.
We're looking for someone who enjoys shipping software, experimenting with new AI capabilities, and solving complex engineering problems while maintaining strong standards for product quality and reliability.
What You'll Do- Design, develop, and deploy AI-powered features across Bask Health's patient-facing applications.
- Build intelligent experiences that help patients navigate onboarding, questionnaires, and digital care workflows.
- Develop AI-assisted interfaces that simplify complex patient interactions.
- Integrate large language models into existing frontend and backend applications.
- Build production services that support patient-facing AI features.
- Design retrieval-augmented generation systems using approved, relevant knowledge sources.
- Implement structured outputs, tool calling, and workflow orchestration for AI applications.
- Develop AI-assisted patient support and information experiences within approved boundaries.
- Collaborate with clinical and product stakeholders to define safe AI behavior and escalation requirements.
- Ensure AI-generated content does not independently replace clinical judgment or prescribing decisions.
- Create safeguards for inaccurate, unsupported, or inappropriate AI-generated responses.
- Build mechanisms for uncertainty handling, human review, and escalation when appropriate.
- Design evaluation datasets and testing procedures for patient-facing AI features.
- Measure response accuracy, relevance, latency, reliability, and user experience.
- Implement monitoring and observability for production AI applications.
- Investigate model failures, unexpected outputs, and performance issues.
- Optimize prompts, retrieval pipelines, and application logic to improve output quality.
- Evaluate AI models and providers based on performance, cost, reliability, and operational requirements.
- Develop secure integrations between AI services and existing Bask platform APIs.
- Work with engineering teams to ensure appropriate authentication, authorization, and data access controls.
- Support privacy-conscious handling of patient information and sensitive application data.
- Collaborate with frontend engineers to deliver responsive, intuitive AI-powered interfaces.
- Build reusable components and backend services for future patient-facing AI features.
- Write maintainable, well-tested code and participate in technical design reviews.
- Contribute to architecture discussions and help establish applied AI engineering standards.
- Mentor other engineers and share practical knowledge about production AI development.
- Use experimentation and product feedback to improve AI-assisted patient workflows.
- Own features from technical design through deployment, monitoring, and iteration.
Requirements- 5+ years of professional software engineering experience, including substantial experience building AI-powered applications.
- Demonstrated experience shipping LLM-powered or machine learning features into production.
- Strong programming skills in Python, TypeScript, JavaScript, or comparable languages.
- Experience integrating large language models through production APIs.
- Strong understanding of modern LLM application architecture.
- Hands-on experience with prompt engineering, structured outputs, and model integration patterns.
- Experience building retrieval-augmented generation systems or similar knowledge-grounded AI applications.
- Familiarity with embeddings, semantic retrieval, and search infrastructure.
- Experience implementing tool calling, workflow orchestration, or multi-step AI processes.
- Strong backend engineering skills, including API design and service integrations.
- Experience working with databases, application state, and production data systems.
- Understanding of model evaluation and AI application testing methodologies.
- Experience measuring and improving AI response quality, latency, and reliability.
- Familiarity with production monitoring, logging, and troubleshooting.
- Experience implementing fallback behavior and error handling for AI-dependent applications.
- Understanding of application security, authentication, authorization, and data privacy.
- Ability to design safeguards for sensitive or high-impact AI workflows.
- Experience collaborating with frontend engineers to deliver end-user product features.
- Strong understanding of software testing, code review, and deployment practices.
- Familiarity with cloud infrastructure and production application environments.
- Ability to translate product requirements into maintainable technical solutions.
- Strong communication skills and experience working across engineering and product teams.
- Ability to independently own complex technical projects from design through production.
- Strong analytical thinking and practical problem-solving skills.
- Interest in building reliable, user-centered AI products.
Nice to Have- Experience developing AI-powered applications in healthcare, telehealth, or digital health.
- Familiarity with HIPAA and healthcare data privacy considerations.
- Experience building patient-facing chat interfaces, assistants, or guided workflows.
- Experience designing AI systems with clinical oversight or human-in-the-loop review.
- Familiarity with healthcare workflows, patient onboarding, and treatment-related communication.
- Experience with OpenAI, Anthropic, or comparable model providers.
- Experience using LangChain, LangGraph, or similar orchestration frameworks.
- Familiarity with vector databases such as Pinecone, Weaviate, or pgvector.
- Experience developing conversational AI systems.
- Experience with agentic AI workflows and tool-using assistants.
- Familiarity with model evaluation frameworks and automated LLM testing.
- Experience with prompt versioning and experiment tracking.
- Experience using React, Next.js, or modern frontend frameworks.
- Experience building backend services with Node.js and TypeScript.
- Familiarity with PostgreSQL, Prisma, Drizzle, or similar data tools.
- Experience deploying applications on AWS or comparable cloud platforms.
- Familiarity with serverless architectures and event-driven systems.
- Experience optimizing LLM inference costs and application latency.
- Experience with AI-powered search, knowledge retrieval, or personalization.
- Experience using AI-assisted development tools such as Cursor or Claude Code.
