Principal Software Engineer - Backend / AI

The Principal Software Engineer leads our largest, most complex projects - making sure they meet a high bar for performance, scalability, and reliability. You'll work closely with other technical leaders, product managers, and cross-functional teams to keep solutions aligned with both business goals and technical realities. This is a hands-on leadership role. You'll shape architecture, mentor engineers across backend, frontend, and our AI platform, and help bring emerging technology - especially agentic AI into the product in ways that genuinely move the needle for customers.

Responsibilities:

• Design and deploy scalable, high-performance solutions across the stack (backend, frontend, and beyond), and establish the architectural standards for reliability, security, and performance the team builds against.
• Help build our AI-native platform - designing agentic workflows that plan, call tools, and run multi-step tasks in production, not just as prototypes.
• Stay close to customers - understand their real workflows, sit with their hardest problems, and carry that context back into the architecture and roadmap.
• Lead and mentor engineers across disciplines, promoting best practices in coding, testing, and deployment.
• Partner with product managers, engineers, and other stakeholders to define requirements and deliver integrated solutions that meet both business and technical needs.
• Own the lifecycle of complex, multi-area projects - ensuring timely, high-quality delivery, resolving hard technical issues, and managing technical debt with the judgment to keep momentum.
• Document key architectural designs and decisions, and share knowledge so the team can build, debug, and learn faster.

Requirements:

• Bachelor's degree in Computer Science or a related field, with 14+ years of experience, including 4+ years in a principal or architect-level role.
• Deep expertise in designing and implementing scalable, distributed systems, with strong command of software design patterns and architectural best practices.
• Expertise in modern programming languages and frameworks (e. g., Python, Node.js, Java).
• Strong knowledge of database systems (e. g., SQL, NoSQL), RESTful APIs, and microservices architecture.
• Hands-on experience building and shipping GenAI or AI-agent capabilities in production - connecting models to tools, APIs, and search to run real multi-step workflows.
• Experience with retrieval and semantic search to ground models in real data is a strong plus, along with good instincts for keeping AI output accurate and cost and latency in check.
• Fluency with cloud platforms (AWS, Azure, or GCP) and containers (Docker, Kubernetes), and comfort with modern DevOps and CI/CD practices.