This role is for engineers who want to build production applications and the AI capabilities behind them. You won‘t just use AI tools day to day, you‘ll build the systems that put AI to work for the business. You‘ll join a tight-knit team of four full-stack engineers, working closely together and with engineering leadership to design and ship the applications, data structures, and AI-enabled workflows that power Salute‘s business. That covers everything from the database and API layer under a feature to the RAG pipelines and agentic workflows that make it smarter.
You‘ll use AI tools to accelerate how you build, but the bar doesn‘t move. We still expect clean architecture, real code review, and systems that hold up long after you ship them. This isn‘t a narrow frontend or backend role. You‘ll work across the stack, model and structure the data your applications need, and increasingly build with vector stores, retrieval-augmented generation, and workflow automation to extend what our products can do.
You‘ll spend roughly 75% of your time building and 25% working directly with stakeholders. That means understanding problems, weighing tradeoffs, and making sure what you ship gets used.
What You‘ll Do
- Design and build full-stack applications end-to-end: from data modeling and backend services through to the interfaces business users rely on.
- Use AI coding tools to accelerate delivery while holding the line on code quality, testing, and long-term supportability.
- Build and integrate AI capabilities into products, including vector stores, retrieval-augmented generation (RAG), and automated workflows.
- Collaborate on architecture and technical decisions with the team, then own execution end-to-end.
- Design and maintain the data structures that support both application features and downstream AI capabilities.
- Deploy and operate services on Azure using infrastructure-as-code (Terraform) and container platforms (Azure Container Apps, Kubernetes).
- Partner directly with business stakeholders to understand needs, weigh tradeoffs, and make sure what you build gets used.
Required Qualifications
- Demonstrated ability to design, build, and ship full-stack applications end-to-end, whether that comes from professional experience, standout project work, or both. We care more about what you‘ve built than how many years you‘ve been building it.
- Strong proficiency across the stack: React/TypeScript (Next.js) on the frontend, Python and/or Node.js on the backend, and relational/NoSQL databases.
- Experience with code review and architecture discussions. You bring strong technical judgment even when you‘re executing on someone else‘s call.
- Experience contributing to technical roadmaps and influencing engineering direction, even without formal authority.
- Comfort working closely with a small, hands-on team on shared technical decisions.
Preferred Qualifications
Infrastructure & Platform
- Hands-on experience with Azure, including Azure Container Apps and Kubernetes/Docker.
- Experience with Infrastructure as Code using tools such as Terraform.
- Able to build and maintain CI/CD pipelines.
- Experience designing for scale, systems that needed to grow beyond a single team or use case.
- Experience delivering systems that meet enterprise SLA requirements: high availability, disaster recovery, audit logging, and regulatory compliance.
- Observability and monitoring experience (e.g., OpenTelemetry, Azure Monitor).
Security & Access
- Understanding of JWT, token minting, and RBAC models.
- Solid foundation in secure development practices.
Application Architecture
- Experience building multi-tenant SaaS services and operator-facing applications.
- Experience with event-driven pipelines and architectures.
- Experience designing and documenting APIs (REST, GraphQL, and/or gRPC) consumed by multiple clients.
- Experience designing federated service architectures, API mesh patterns, or event-driven integration layers that decouple siloed applications and enable cross-domain interoperability at enterprise scale.
AI & Automation
- Familiarity with AI/ML integration patterns: vector databases, RAG, LLM/inference APIs, or workflow orchestration.
- Experience using AI coding assistants (e.g., GitHub Copilot, Claude Code, OpenAI Codex) to accelerate development without sacrificing code quality.
Domain & Background
- Experience building platforms in data center, industrial IoT, infrastructure management, or other operations-heavy domains.
- Familiarity with BMS/SCADA integration.
- Degree in Computer Science, Software Engineering, or a related field.