Senior Engineering Manager

Role Overview We are looking for an Engineering Manager / Senior Engineering Manager to lead and scale high-performing engineering teams building modern, AI-driven products. This role requires strong people leadership , solid full-stack awareness with a backend expertise( preferred) , and hands-on experience building LLM-based agents or agentic AI systems . You will manage 15–20 engineers , drive execution, and provide technical leadership for scalable, production-grade platforms. Key Responsibilities Engineering Leadership & People Management Lead, mentor, and manage 15–20 engineers across multiple teams. Own hiring, onboarding, performance management, and career development. Build a culture of engineering excellence, accountability, and high ownership. Balance strategic thinking with hands-on technical guidance when needed. Technical Ownership & Architecture Provide technical leadership across frontend and backend systems. Drive architecture, system design, and technical decision-making. Review designs and critical code paths; guide teams through complex engineering challenges. Ensure systems are scalable, secure, reliable, and observable. AI / Agentic Systems Lead or contribute to building LLM-based agents or agentic AI products . Experience with agent orchestration, tool usage, context management, and evaluation. Exposure to voice bots / conversational AI is a strong plus. Delivery & Execution Own delivery commitments and sprint execution across POD s. Partner with Product Managers to translate requirements into execution plans. Proactively identify risks and ensure predictable, high-quality delivery. Preferred Technical Skills (First Preference) Frontend (UI): React / JavaScript Backend (Preferred): Java with Spring Boot Python with FastAPI Candidates with strong experience in other frontend or backend technologies are also welcome; however, the above stack is strongly preferred . Other Technical Skills Experience designing and scaling distributed systems and APIs. Experience with cloud platforms (AWS or GCP). Familiarity with CI/CD, DevOps practices, and observability tools. Exposure to AI/ML platforms, prompt engineering, or MLOps is a plus.