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AI Summary
Role: Staff Software Engineer
Job Summary
As a Staff Software Engineer, you will lead the design and delivery of high-impact, scalable web applications while shaping the technical direction of the team. You will serve as a trusted technical leader, driving architectural excellence, mentoring engineers, and accelerating development through AI-first engineering practices. This role requires a balance of hands-on development, system design, and cross-team influence to deliver resilient, high-quality solutions in a fast-paced, iterative environment.
Key Responsibilities
Technical Leadership & Architecture
Own the design and evolution of scalable, secure, and high-performing web application architectures
Lead complex feature delivery across systems, ensuring alignment with long-term platform strategy
Act as a technical advisor to engineering and product leadership on design trade-offs and implementation approaches
Drive engineering best practices across code quality, testing, observability, and system reliability
AI-First Engineering Transformation
Champion AI-powered development by actively leveraging tools (e.g., Cursor, Claude Code, LLM platforms) to enhance productivity
Define standards and best practices for AI-assisted coding, test generation, and design-to-code workflows
Lead adoption of generative AI and LLM integrations into enterprise applications and engineering processes
Partner with teams to operationalize AI-driven workflows and improve development efficiency at scale
Development & Delivery
Design, develop, and maintain full-stack web applications with a focus on performance, usability, and reliability
Contribute high-quality, production-grade code across frontend, backend, and APIs
Drive adoption of modern engineering practices including microservices, cloud-native development, and CI/CD
Mentorship & Influence
Mentor senior and mid-level engineers, fostering technical growth and engineering rigor
Conduct in-depth code reviews, providing actionable and constructive feedback
Influence engineering culture by promoting collaboration, ownership, and continuous improvement
Lead by example in driving accountability, technical excellence, and innovation
Required Skills & Competencies
Core Technical Skills (Staff-Level Depth)
Advanced expertise in software design, system architecture, and distributed systems
Strong experience in API design and management (REST/gRPC, versioning, scalability)
Deep understanding of cloud platforms (Azure or AWS) and cloud-native application design
Solid foundation in application security, data protection, and secure coding practices
Strong problem-solving and debugging skills across large-scale systems
AI & Modern Engineering
Hands-on experience integrating LLMs, generative AI, and AI APIs (e.g., OpenAI, Anthropic, Azure OpenAI, AWS Bedrock)
Experience applying AI across SDLC: code generation, testing, documentation, and workflow automation
Understanding of ML concepts and AI adoption strategies across engineering teams
Collaboration & Leadership
Proven ability to influence without authority and drive alignment across teams
Strong communication skills, with the ability to translate complex technical concepts to diverse stakeholders
High adaptability in fast-moving, ambiguous environments
Commitment to ethical engineering practices and responsible AI use
Qualifications (Education & Experience)
10+ years of experience in software engineering with a strong focus on web and distributed systems
Demonstrated success in leading complex technical initiatives and delivering at scale
Proven track record mentoring engineers and elevating team performance
Experience driving adoption of new technologies and engineering transformations (preferably AI-driven)
Experience operating in iterative, product-driven environments
Technical Expertise
Strong full-stack development experience with:
Backend: C#, .NET, Python
Frontend: React (or modern SPA frameworks)
Data: SQL Server, database design, data modeling
Deep experience with Web APIs and microservices architectures
Hands-on experience with cloud-native development and distributed systems
Familiarity with containerization and orchestration (Docker, Kubernetes)
Strong understanding of DevOps practices and CI/CD pipelines
Nice to Have
Certifications in Cloud (Azure/AWS) or AI/ML
Experience building enterprise-scale AI platforms or internal developer tooling
Exposure to platform engineering, developer productivity, or internal frameworks
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