Principal Engineer

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. 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. Key Responsibilities

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.

Security & Compliance Partner with InfoSec to embed security-by-design across all AI/ML workloads. Implement controls to protect sensitive enterprise data while meeting global compliance standards (SOC2, ISO 27001, GDPR, DPDP).

Collaboration & Leadership Work closely with the Head of AI to translate cutting-edge research into production-grade platforms . Provide technical mentorship to engineering teams, ensuring best practices in distributed systems and infra design. Evaluate and adopt emerging technologies (e.g., SSMs, inference optimizers like Triton, Riva, vLLM ) to stay ahead of the curve.

Required Qualifications & Skills

10 - 15 years of experience in large-scale systems architecture, with at least 5 years in principal architect-level roles. Proven expertise in distributed systems, cloud-native architectures, and real-time pipelines . Hands-on experience with containerization, orchestration (Kubernetes), and microservices . Strong background in scalable ML infrastructure , including model serving, GPU/accelerator utilization, and CI/CD for ML. Demonstrated ability to architect systems with low latency (<300ms), high throughput, and enterprise reliability . Experience in conversational AI, speech systems, or real-time inference workloads . Deep knowledge of MLOps platforms (Kubeflow, MLflow, VertexAI, SageMaker). Familiarity with state-of-the-art inference optimization frameworks (e.g., Triton, Nvidia Riva, vLLM, SGLang). Open-source contributions or patents in distributed systems, infra, or ML tooling.