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Staff Software Engineer

Fortive
4 days ago
Full-time
Remote
United States
Engineer
Description

We are looking for an experienced Tech Lead (Staff Software Engineer) to lead a high-impact engineering team building scalable, cloud-native applications with modern DevOps practices. You will own technical strategy, architecture decisions, and delivery while mentoring engineers and driving innovation in both traditional software engineering and AI-powered solutions. This is a hands-on leadership role — you will code, design systems, and set the technical direction for critical initiatives.

Key Responsibilities

  • Lead and mentor a team of software engineers, setting technical standards and best practices
  • Architect and deliver scalable cloud systems using Infrastructure as Code (Terraform)
  • Design and implement full-stack features leveraging TypeScript (Node.js/React or similar) and C# (.NET)
  • Own end-to-end DevOps pipelines — CI/CD, monitoring, infrastructure automation, and reliability
  • Integrate AI engineering capabilities into products (LLM integration, generative AI features, AI agents, RAG pipelines, etc.)
  • Collaborate with product and cross-functional teams to translate business needs into technical solutions
  • Conduct code reviews, architectural reviews, and drive continuous improvement in code quality, performance, and security
  • Champion cloud best practices, cost optimization, and platform modernization initiatives

Required Qualifications

  • 8+ years of professional software engineering experience (3+ years in a technical leadership or Staff+ role)
  • Strong expertise in Cloud platforms (AWS and Azure — multi-cloud experience a plus)
  • Advanced proficiency with Terraform for infrastructure provisioning and management at scale
  • Deep hands-on experience with TypeScript (Node.js ecosystem, TypeScript best practices, modern frameworks)
  • Strong command of C# and the .NET ecosystem (ASP.NET Core, microservices, APIs)
  • Proven DevOps expertise: GitHub Actions / Jenkins, Docker/Kubernetes, monitoring, and infrastructure automation
  • Solid understanding of AI engineering concepts and practical implementation (LLM APIs, prompt engineering, vector databases, model orchestration, or building AI-powered features)
  • Excellent communication and leadership skills — ability to influence without authority and mentor senior engineers