Software Engineer - AI Platform

Software Engineer, AI Platform

Bask Health · United States (Remote) · Full-time · Posted 2026-11-12

Workplace: remote

Department: AI & Machine Learning

Description

About Bask Health

Bask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.

Our platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.

We're an entrepreneurial, product-focused engineering team that values ownership, technical excellence, and building software that solves real problems. Engineers work closely with product teams and company leadership, take features from idea to production, and have a direct role in shaping the platform.

Engineering at Bask is AI-first and builder-led. We use tools like Cursor and Claude Code throughout development while maintaining high standards for reliability, testing, and software quality.

About the Role

We're looking for a Software Engineer, AI Platform to build and maintain the foundational software infrastructure that supports AI-powered applications across Bask Health.

As Bask expands its use of artificial intelligence across merchant tools, patient experiences, clinical workflows, pharmacy operations, and internal systems, we need reliable shared services that allow engineering teams to develop and deploy AI capabilities efficiently.

This role focuses on the platform infrastructure behind those applications. You'll build reusable APIs, model integration services, inference gateways, data access components, and operational tools that make it easier for other engineers to incorporate AI into production software.

You'll work with backend technologies, cloud infrastructure, databases, model providers, and distributed systems to develop scalable services that handle AI requests, manage application resources, and support reliable execution.

Your work may include centralized model access, request routing, usage tracking, authentication, rate limiting, prompt configuration, observability, and infrastructure that supports retrieval and agent-based applications.

You'll collaborate with applied AI engineers, machine learning engineers, product engineers, and infrastructure specialists to understand their technical requirements and develop platform capabilities that can be reused across multiple products.

Because Bask operates in healthcare-related environments, you'll also help ensure AI infrastructure respects privacy requirements, tenant isolation, access controls, and approved data-handling practices. You'll design systems that allow teams to innovate without compromising security or reliability.

This is a hands-on engineering role with ownership over production infrastructure and shared developer capabilities. You'll write code, improve system architecture, investigate operational issues, and help establish engineering patterns that support Bask's growing AI capabilities.

We're looking for an engineer who enjoys building reliable backend systems, thinks carefully about scalability and developer experience, and understands how strong infrastructure enables teams to ship better products.

What You'll Do

• Design, build, and maintain shared infrastructure for AI-powered applications across Bask Health.
• Develop backend services and APIs that support model integration and AI application execution.
• Build reusable components that allow engineering teams to access large language models consistently.
• Develop centralized model integration services and inference request handling.
• Implement routing and configuration capabilities for multiple model providers.
• Build systems for managing model API credentials, authentication, and access permissions.
• Develop request validation, structured output handling, and standardized error responses.
• Implement rate limiting, retries, timeouts, and fallback mechanisms for model API requests.
• Build usage tracking and cost monitoring for AI services.
• Develop infrastructure that supports prompt configuration, versioning, and controlled updates.
• Create shared services for embeddings, retrieval, and knowledge-grounded AI applications.
• Build APIs and integration patterns that support AI agents and workflow automation.
• Develop secure data access components for AI applications.
• Ensure AI services respect tenant boundaries and application authorization requirements.
• Build observability systems for model requests, latency, errors, and operational performance.
• Implement structured logging and distributed tracing across AI services.
• Develop automated testing and deployment pipelines for AI platform components.
• Improve infrastructure reliability, fault tolerance, and service availability.
• Optimize backend performance, throughput, and infrastructure costs.
• Design scalable asynchronous processing and background job systems.
• Integrate AI platform services with existing application databases and backend infrastructure.
• Build internal developer tools and documentation that simplify AI application development.
• Collaborate with applied AI engineers to identify common infrastructure needs.
• Support machine learning engineers with reliable model-serving and evaluation integrations.
• Investigate production incidents and resolve infrastructure-related failures.
• Participate in architecture discussions, technical planning, and code reviews.
• Establish reusable engineering patterns for secure and maintainable AI integrations.
• Own assigned platform services from technical design through deployment, monitoring, and continuous improvement.

Requirements

• 3+ years of professional software engineering experience, preferably in backend, platform, or infrastructure engineering.
• Demonstrated experience building and maintaining production backend services.
• Strong programming skills in TypeScript, JavaScript, Python, or comparable backend languages.
• Experience designing and developing APIs and service integrations.
• Strong understanding of backend application architecture and distributed systems fundamentals.
• Familiarity with large language models and modern AI application integration patterns.
• Experience integrating third-party APIs or external services into production applications.
• Understanding of authentication, authorization, and secure service communication.
• Experience working with relational databases and application data models.
• Familiarity with asynchronous processing, background jobs, and event-driven systems.
• Experience designing systems that handle errors, retries, timeouts, and service failures.
• Understanding of cloud infrastructure and production deployment practices.
• Familiarity with automated testing and continuous integration workflows.
• Experience working with logging, monitoring, and production observability tools.
• Understanding of system performance, latency, scalability, and reliability.
• Experience designing reusable software components and shared engineering services.
• Familiarity with API versioning, configuration management, and service maintenance.
• Understanding of data privacy and secure application development.
• Experience using Git, code review, and collaborative software development practices.
• Ability to troubleshoot complex backend and infrastructure issues.
• Experience writing maintainable, well-documented production code.
• Ability to evaluate engineering tradeoffs involving performance, complexity, and operational costs.
• Experience collaborating with engineers across multiple product or technical teams.
• Strong technical problem-solving and analytical skills.
• Ability to independently own engineering projects from design through deployment.

Nice to Have

• Experience building infrastructure for AI or machine learning applications.
• Familiarity with OpenAI, Anthropic, or comparable model APIs.
• Experience building model gateways, inference proxies, or centralized AI integration services.
• Familiarity with multi-model routing and provider fallback strategies.
• Experience with TypeScript and Node.js backend development.
• Familiarity with tRPC, Prisma, Drizzle, or similar application development tools.
• Experience working with AWS Lambda, SQS, EventBridge, or comparable cloud services.
• Familiarity with serverless infrastructure and infrastructure-as-code tools.
• Experience with SST or similar application deployment frameworks.
• Familiarity with containerization and distributed service deployment.
• Experience building systems for API rate limiting, usage metering, or cost allocation.
• Familiarity with embeddings, vector databases, and retrieval infrastructure.
• Experience supporting retrieval-augmented generation applications.
• Experience building shared infrastructure for AI agents or automated workflows.
• Familiarity with prompt management, versioning, and configuration systems.
• Experience implementing AI request tracing and model observability.
• Experience developing internal SDKs, libraries, or developer platforms.
• Understanding of multi-tenant SaaS architecture and data isolation.
• Familiarity with HIPAA and healthcare data privacy considerations.
• Experience using AI-assisted development tools such as Cursor or Claude Code.

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