Technical Lead

Job Description

Tech Lead(Fullstack) Nexa (Conversational Voice AI Platform). Location : Bangalore Type: Full-time. Experience : 4+ years (preferably in early-stage startups). Tech Stack : Python (core), Node.js, React.js. We're looking for a Tech Lead to join us at the ground level. This is a high-ownership, high-speed role for builders who want to move fast and go deep.. What You'll Do. Design, build, and scale backend and full-stack systems for our voice AI engine. Work primarily with Python (core logic, pipelines, model integration), and support full-stack features using Node.js and React.js. Lead projects end-to-end-from whiteboard to production deployment. Optimize systems for performance, scale, and real-time processing. Collaborate with founders, ML engineers, and designers to rapidly prototype and ship features. Set engineering best practices, own code quality, and mentor junior team members as we grow. Must-Have Skills. 4+ years of experience in Python, building scalable production systems. Has led projects independently, from design through deployment. Excellent at executing fast without compromising quality. Strong foundation in system design, data structures and algorithms. Hands-on experience with Node.js and React.js in a production setup. Deep understanding of backend architecture-APIs, microservices, data ows. Proven success working in early-stage startups, especially during 01 scaling phases. Ability to debug and optimize across the full stack. High autonomy-can break down big problems, prioritize, and deliver without hand-holding. Ownership mindset: You act like a founder-even if you're not one. Technical depth: You've built things from scratch and understand what's under the hood. Product intuition: You don't just write code-you ask if it solves the user's problem. Nice-to-Have. Experience with NLP, speech interfaces, or audio processing. Familiarity with cloud platforms (GCP/AWS), CI/CD, Docker, Kubernetes. Contributions to open-source or technical blogs. Prior experience integrating ML models into production systems.