Software Engineer, AI Platform

Bask Health

. Design, build, and maintain shared infrastructure for AI-powered applications across Bask Health .

Posted 11/12/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in backend service development, API design, and integration for AI applications, with a strong focus on security, performance, and reliability. Proficient in managing infrastructure and collaborating with cross-functional teams to enhance AI-powered solutions.

Highest-signal resume keywords

Backend Service DevelopmentAPI Design and IntegrationTypeScript ProgrammingCloud Infrastructure ManagementAutomated Testing and CI/CD

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Applicant Tracking System Keywords

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Hard Skills

TypeScriptJavaScriptPythonAPI DevelopmentDistributed SystemsRelational DatabasesAsynchronous ProcessingError HandlingLogging and MonitoringInfrastructure Reliability

Soft Skills

Technical Problem-SolvingAnalytical SkillsCollaborationProject OwnershipCommunication

Tools & Technologies

GitAWS LambdaSQSEventBridgeTRPCPrismaDrizzleServerless InfrastructureContainerizationAI-Assisted Development Tools

Industry Keywords

AI ApplicationsMachine LearningData PrivacyMulti-Tenant SaaSHealthcare Data Privacy

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsJavaScriptNode.jsPythonTypeScript

About the role

Key responsibilities & impact

Design, build, and maintain shared infrastructure for AI-powered applications across Bask Health
Develop backend services and APIs supporting model integration and AI application execution
Build reusable components for consistent large language model access
Develop centralized model integration services and inference request handling
Implement routing and configuration for multiple model providers
Manage 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 prompt configuration, versioning, and controlled update infrastructure
Create shared services for embeddings, retrieval, and knowledge-grounded AI applications
Build APIs and integration patterns supporting AI agents and workflow automation
Develop secure data access components for AI applications
Ensure tenant boundaries and application authorization requirements are respected
Build observability 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, service availability, backend performance, throughput, and infrastructure costs
Design scalable asynchronous processing and background job systems
Integrate AI platform services with application databases and backend infrastructure
Build internal developer tools and documentation simplifying AI application development
Collaborate with applied AI engineers and support machine learning engineers with model-serving and evaluation integrations
Investigate production incidents and resolve infrastructure-related failures
Participate in architecture discussions, technical planning, and code reviews
Establish reusable patterns for secure and maintainable AI integrations
Own platform services from technical design through deployment, monitoring, and continuous improvement

Requirements

What you’ll need

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