Full-Time Principal Backend Software Engineer

• Define and own the long-term technical vision and architectural roadmap, aligning it with business and product strategy.
• Drive the selection and governance of foundational technologies, frameworks, and architectural patterns.
• Future-proof the platform by anticipating technical challenges and industry trends.
• Establish and enforce engineering-wide best practices for architecture, code, and design.
• Drive initiatives to reduce technical debt and enhance platform scalability, security, performance, and observability.
• Serve as the decision-maker on critical system designs, ensuring architectural cohesion across teams.
• Mentor senior engineers and tech leads, elevating the team's architectural and problem-solving skills.
• Lead hands-on by prototyping solutions and solving the most complex technical challenges.
• Cultivate a culture of technical excellence and innovation across the engineering organization.
• Translate ambiguous business needs into clear technical strategies by partnering with executive, product, and enterprise leaders.
• Communicate complex architectural decisions to all stakeholders to build consensus and drive company-wide alignment.

• 10-15+ years of experience designing, building, and operating large-scale, distributed, cloud-native platforms.
• Proven experience as a Principal/Staff Engineer or Senior Architect, setting technical direction and making high-impact architectural decisions across multiple teams.
• Proficiency in programming languages such as Python, Go, or Typescript, with a strong ability to write clean, maintainable code.
• Expertise in modern cloud architecture (GCP, AWS, Azure), containerization (Docker, Kubernetes), and IaC (Terraform).
• Deep knowledge of scalable architectures, API design (REST, gRPC), and event-driven systems.
• A systems-thinking approach to problem-solving and making strategic trade-offs (performance, cost, security).
• Exceptional communication skills with a proven ability to drive technical alignment.
• Significant plus: Experience with infrastructure for ML Ops (model training, deployment, monitoring).
• BS/MS in Computer Science or a related field, or equivalent experience.

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