Senior Software Engineer - Back End (Remote)

Ad Hoc LLC is a technology company that empowers organizations to deliver scalable, impactful digital services. The Senior Software Engineer - Back End will build robust infrastructure that integrates health content with AI capabilities and ensures reliable service delivery for public health applications.

Responsibilities:

• The Backend Engineer will build the robust, scalable infrastructure that bridges health content with modern AI capabilities and near/real-time data streams
• They'll create backend services that can orchestrate complex workflows from processing user queries through AI models to falling back on traditional search, from ingesting real-time surveillance data to caching AI responses for performance
• This engineer will implement critical patterns like circuit breakers for AI service failures, rate limiting for expensive model calls, and audit logging for AI interactions that may need review
• Working at the intersection of traditional web services and AI systems, they'll build APIs that can handle the unique requirements of AI features: streaming responses for conversational interfaces, batch processing for content enrichment, webhook handlers for model training feedback, and session management for multi-turn interactions
• They'll ensure the backend can gracefully handle AI uncertainties including timeouts, hallucinations, or service outages, while maintaining the reliability users expect from public health sites
• Their work creates the flexible foundation that allows the site to experiment with innovative AI features while ensuring that core health information remains accessible even when advanced features fail, ultimately enabling evolved digital services without compromising on performance or reliability
• Demonstrates expertise, accountability, and ownership of their contributions to an application code base
• Maintains a high-level understanding of adjacent technical areas and ecosystems to effectively coordinate integration with those systems
• Diagnoses and effectively resolves issues with the systems they own, using incidents to inform educational opportunities and system improvements
• Actively mentors and assists more junior engineers in the development of their skills
• Effectively communicates technical issues and developments with team members and clients
• Participates in technical interviews with new candidates
• Strong influential skills to propose and evaluate multiple approaches to technical and process problems
• May serve as a mentor to individuals within the team
• May leads small, less critical, or temporary team structures and projects
• Presents design documents, system diagrams, etc. to clients, stakeholders, partners, and other engineers

Requirements:

• Bachelor's degree and 7+ years of engineering experience
• Relevant years of experience may be substituted for education
• Node.js or Python (Express/Fastify or FastAPI/Django) with WebSocket support - For building APIs that can handle both traditional REST requests and real-time streaming from AI models or live data feeds
• PostgreSQL with JSON/JSONB and full-text search capabilities - Storing structured health content alongside flexible AI-generated data, user interactions, and feedback loops
• Message queue systems (Redis with Bull/Celery, or AWS SQS) - Essential for handling asynchronous AI processing, batch content updates, and managing rate limits for external AI services
• CMS development (Wordpress, Drupal) - Essential for extending CMS capabilities and building content APIs
• AWS/Azure core services (EC2/App Service, RDS/Azure SQL, Lambda/Functions, S3/Blob Storage) - For building cloud-native applications that scale automatically and integrate with managed services
• Infrastructure as Code (Terraform basics, CloudFormation, ARM templates) - For defining and deploying backend infrastructure consistently across environments
• Container orchestration (ECS/Fargate, AKS/Container Instances, basic Kubernetes concepts) - For deploying and scaling containerized services
• Basic NLP libraries and text processing (spaCy, NLTK, or understanding of tokenization/embedding concepts) - For preprocessing content for AI consumption and implementing fallback search when AI features are unavailable
• GraphQL with subscription support - For flexible data fetching that could serve diverse AI features and real-time updates to frontend applications