Principal Software Engineer

• AI-Native Development: Improve AI tools and practices across the SDLC, own the quality of AI-generated artifacts, and embed Responsible AI controls and engineering-health metrics (e.g., Accelerate/SPACE/ESSP). 

• Coding: Lead and mentor teams to deliver secure, performant, well-tested, maintainable code—using modern coding patterns (including GenAI where appropriate), strong diagnostics/telemetry, and disciplined reviews/retrospectives to prevent and resolve production issues. 

• Design: Own architecture and design decisions for complex systems, evaluating options and tradeoffs to meet security, privacy, scalability, resiliency/DR, cost, and testability requirements while coordinating dependencies across teams. 

• Engineering Excellence: Drive best practices, learning, and developer tooling to raise engineering standards—especially around security/compliance evidence, “security as code,” automation, and scalable, reliable delivery. 

• Implement: Plan and execute delivery with experimentation, clear success and guardrail metrics, secure rollout/rollback strategies, and automated deployments aligned to schedules and operational readiness. 

• Reliability and Supportability: Build and improve observability and on-call readiness (logging, telemetry, dashboards, playbooks), lead incident response/postmortems, and reduce incident volume through systemic fixes and better troubleshooting assets. 

• Understand User Requirements: Partner with stakeholders to validate user requirements and feasibility, capture explicit and implicit needs (including security/privacy), and define measurable feedback loops and metrics that guide future improvements.

Required Qualifications and Skills 

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 

• 12+ years of professional software engineering experience, including hands‑on development in one or more modern programming languages but not limited to C#, Java, Python. 

• Strong understanding of core software engineering fundamentals, including data structures, algorithms, object‑oriented design, and scalable system principles. 

• Experience developing, testing, debugging, and maintaining production‑quality code, following established coding standards and development processes. 

• Working knowledge of cloud, distributed systems, or service‑based architectures, with exposure to reliability, performance, and monitoring concepts. 

• Designing and Architecting Business Solutions: Designing and architecting products, services, and systems by employing design principles across diverse technological solutions and guiding projects through each phase of the engineering lifecycle to ensure alignment with business goals and customer satisfaction. 

• Driving Solution Improvement and Excellence: Leading the validation of innovative products, services, and solutions through pinpointing opportunities for improvement, monitoring and testing systems, and fostering strategic solutions to identified challenges or issues. 

• Engineering Robust Software Solutions: Employing computer science principles to design, develop, and optimize scalable algorithms, distributed systems, business applications, and other software solutions. 

• Solving Problems Through a Data-Driven Approach: Extracting actionable insights to understand their implications within the broader organizational context; solving problems and making decisions through a swift, informed, and strategic approach. 

Preferred qualification: 

• 14+ years of hands‑on software engineering experience with coding in languages such as C#, Python, demonstrating strong fundamentals and growing depth in building production‑quality software. 

• Solid technical judgment and problem‑solving skills, including the ability to design and implement scoped solutions, debug complex issues, refactor code, and improve reliability and performance of services.  

• Ability to collaborate effectively and influence peers, navigating team dynamics, participating constructively in design and code reviews, and contributing positively to team engineering culture. 

• Demonstrated collaboration and emerging leadership skills, such as mentoring engineers, owning well‑defined components or features, and supporting operational excellence when needed.  

• Exposure to AI platforms, large language models (LLMs), and core machine‑learning concepts, with interest or early experience applying them to real‑world software engineering problems.