The posting, in Meraki-Labs's own words
archived Sep 9, 2026About Nurix AI
At Nurix AI, we are pioneering the Autopilot Enterprise. Our conversational AI agents handle workflows, drive outcomes, and deliver measurable impact for businesses. Born from the belief that enterprises need a new playbook, we build autonomous, multilingual agents capable of complex reasoning, contextual understanding, and end-to-end workflow ownership. Backed by $27.5M in funding from Accel, General Catalyst, and Meraki Labs, and led by Mukesh Bansal, we are India's first scaled enterprise AI company, delivering cutting-edge AI solutions that integrate seamlessly into workflows across industries like Retail, Insurance, Education & Home Services. Join us in shaping the future of enterprise AI — where every interaction is smarter, faster, and human-like.
About the Role
As Principal Engineer at Nurix AI, you will be the cornerstone of our technical infrastructure, enabling our AI agents to scale reliably and securely in production. You will design and oversee distributed systems that deliver low-latency, high-availability voice and chat AI, while meeting enterprise-grade security and compliance requirements. This is a hands-on leadership role focused on architecture, systems design, and performance engineering — ensuring that Nurix's groundbreaking AI research translates into robust, real-world deployments.
Read the full posting ↓
Systems Architecture & Scalability
Design and evolve the end-to-end infrastructure supporting ASR/TTS, LLM orchestration, Agentic RAG, and self-learning workflows. Architect low-latency pipelines for real-time conversational AI, ensuring sub-second response times across voice and chat. Build multi-cloud, distributed systems (AWS, GCP, Azure) with elastic scaling to handle spiky workloads.
Reliability & Performance Engineering
Define and enforce SLAs around latency, uptime, and throughput for AI services. Drive observability, monitoring, and resilience strategies to handle failures gracefully. Optimize GPU/TPU utilization for cost-effective training and inference.