Principal Software Engineer (AI-Augmented Cloud Platforms)-REMOTE

Aries Solutions Intl Inc is seeking a Principal Software Engineer specializing in AI-Augmented Cloud Platforms. The role involves designing and delivering software solutions, mentoring peers, and leveraging AI tooling to enhance development workflows.

Responsibilities:

• Design and deliver software solutions across the full engineering lifecycle — from architecture and infrastructure through to application logic, integration, testing, and deployment
• Contribute to platform and tooling decisions, not just implementation
• Bring engineering rigor to every layer of the stack: infrastructure, backend services, APIs, front-end experiences, pipelines, and automation
• Write code that is production-ready from the start — tested, documented, observable, and maintainable
• Make deliberate use of AI tooling throughout your development workflow and help raise the standard for how the wider team works with AI
• Mentor and influence peers through code review, pair programming, technical documentation, and architectural input
• Move fluidly between domains as delivery demands it, while going deep where depth is needed

Requirements:

• Strong grasp of enterprise application architecture, distributed systems design, and integration patterns
• Experience applying domain-driven design, clean architecture, or equivalent structural approaches to real production systems
• Comfort reasoning about trade-offs — coupling, cohesion, consistency, scalability, operability — and articulating those trade-offs to stakeholders
• You have a working toolbox built up over years of real delivery
• Languages and frameworks: .NET / C#, Python, JavaScript / TypeScript, PowerShell, Angular
• Cloud and infrastructure: Microsoft Azure, Terraform, Infrastructure as Code (IaC), cloud-native architecture patterns
• DevOps and delivery: GitHub, GitHub Actions, CI/CD pipeline design and implementation
• Cross-cutting: API design, observability, security practices, dependency management
• Developer-led testing. You write tests as part of development, not after
• Documentation as a first-class output. Code is written once and read many times
• Secure by default. You apply security thinking throughout the development lifecycle
• Operational awareness. You build with logging, observability, and operability in mind
• You use AI tooling actively across your development workflow — code generation, review, documentation, debugging, exploration, test scaffolding
• You understand enough about how large language models work — context windows, prompting patterns, retrieval-augmented generation, agent architectures
• You are proficient with current AI development tooling: IDE-integrated assistants, agentic workflows, prompt engineering, and the emerging ecosystem of AI-native developer tools
• You can communicate your AI workflow to others clearly — what you use, when you use it, what you still do yourself, and what safety or quality checks you apply to AI-assisted output
• Significant professional experience in software engineering with demonstrable delivery across multiple domains, platforms, and team contexts
• A track record of technical leadership — you have influenced architecture decisions, shaped delivery practices, and helped teams' level up
• Experience in cloud-native delivery environments, with real ownership of infrastructure and pipelines, not just application code
• Evidence of continuous learning: certifications, open-source contributions, internal engineering initiatives, or simply a clear pattern of expanding your capability over time