Principal Software Engineer - Backend & Infrastructure at Level AI, a Series C conversational intelligence company. Own architecture for real-time data processing, ML platform, and GPU infrastructure. Mentor senior engineers and set technical direction across Noida and Bengaluru teams.

About the Company

Level AI is a Series C conversational intelligence company headquartered in Mountain View, CA, backed by top-tier venture capital firms and Silicon Valley operators. The company empowers enterprise contact centers to understand every customer conversation by leveraging speech AI, NLP/NLU, and large-scale retrieval systems. Level AI transforms millions of unstructured interactions into actionable business decisions. The team includes engineers from Amazon, Google, and Meta who joined because of the unsolved problems and real ownership. With offices in Mountain View and Noida/Bengaluru, Level AI is at a critical scaling inflection point from Series C to the next growth stage.

Role Overview

This is a Principal level role for a seasoned backend and infrastructure engineer who will own the architectural transition as the company scales. The Principal Software Engineer will set the technical direction for backend and ML infrastructure across multiple teams, make high-impact architectural decisions that are expensive to reverse, and raise the engineering bar through design reviews, mentorship, and setting standards by example. Reporting to the VP of Engineering, this role partners directly with ML, Product, and Infrastructure leads across both US and India sites. Success in the first year includes mapping critical paths and failure modes within 90 days, owning a published technical roadmap by six months, and ensuring infrastructure scales predictably with customer growth while keeping inference costs flat or falling as volume rises.

Key Responsibilities

• Own the architecture for real-time data processing at scale. Design and evolve distributed messaging systems that handle high-throughput streaming workloads with strict latency requirements.
• Build the ML platform that enables faster model shipping. Define and execute the technical roadmap for training and serving infrastructure as models grow in size, complexity, and inference cost.
• Scale GPU infrastructure. Own capacity planning, scheduling, and utilization across training and inference fleets, deciding resource allocation and burst handling while controlling spend.
• Scale inference. Drive down latency and cost per request through batching, routing, quantization, compilation, autoscaling, and caching without degrading output quality.
• Make reliability a system property. Drive uptime, observability, and incident response for enterprise-grade serving systems.
• Turn ambiguous business problems into executable technical plans. Partner with Product and GTM to scope cross-functional initiatives and break them into work for other teams.
• Multiply the team's effectiveness. Lead design reviews, mentor senior engineers, and shape engineering best practices.
• Bring the outside in. Evaluate emerging tools and techniques with judgment, adopting what earns complexity and skipping unnecessary overhead.

Tech Stack

The role requires deep, hands-on experience with the following technologies and infrastructure components:

• Backend frameworks: Django
• Task queues & messaging: Celery
• Databases & caching: PostgreSQL, Redis
• Cloud platform: Google Cloud (GCP)
• GPU & inference infrastructure: GPU clusters, vLLM, TensorRT, NVIDIA Triton Inference Server, Ray Serve
• Large-scale systems: High-throughput messaging systems, real-time job queues, distributed databases
• Additional: Speech AI, NLP/NLU, information retrieval systems, ML platform tooling

10+ years building backend and infrastructure systems with a track record of owning architecture and design at scale. Deep hands-on experience with large-scale databases, high-throughput messaging systems, and real-time job queues. Proven ability to navigate complex codebases and reason about architectural tradeoffs. Experience mentoring senior engineers and driving technical decisions through influence. Strong written communication for making technical and business cases. BTech/MTech/PhD in Computer Science or equivalent. Bonus: Production experience with Django, Celery, Redis, PostgreSQL, Google Cloud; scaling GPU infrastructure and model inference; expertise in vLLM, TensorRT, Triton, Ray Serve; experience scaling through Series C to D growth stage; background in speech, NLP, or IR.

Health coverage for you, your spouse, children, and parents; home-office and connectivity allowance; annual learning budget; parental leave.