About the role
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 initiativesRequired 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)