Founding AI Engineer – AI Infra

2 days ago

Bengaluru, India Knowl Full time
About Knowl

Knowl is building the technology stack for the world’s most intelligent call centre conversations—powered by Generative AI to deliver human-like interactions and real-time decisioning at massive scale.

We’re a fast-growing, VC-backed startup trusted by India’s largest fintechs, NBFCs, banks, and insurers.

  • 2M+ AI-driven calls every day
  • Sub-500ms latency for seamless, natural conversations
  • Deep tech stack across LLMs, speech infra, and real-time systems
The Impact You Will Create

As a founding engineer, you’ll dive into one or more of our hardest challenges: vertical LLMs, low-latency voice infra, and distributed intelligence systems.

  • LLM Infrastructure → Build and optimise in-house models, training pipelines, and inference systems for scale and low latency.
  • Voice Systems → Push the limits of real-time speech infra with low-latency audio streaming and cutting-edge detection techniques.
  • Distributed Intelligence → Architect data replication pipelines, scheduling, and logging systems that handle millions of conversations daily.

Every line of code you write will power millions of AI-driven conversations and directly impact some of the largest financial institutions in India.

You’re a Great Fit If You:
  • Have cracked tough real-world problems at IITs/NITs/BITS or at top companies such as Google, Nvidia, DeepMind, etc.
  • Have a strong foundation in the fundamentals of computer science
  • Have strong proficiency with at least one object-oriented programming language.
  • Have experience in development using the services of at least one cloud platform
  • Have an understanding of the basics of distributed systems

BONUS:

  • Have experience in writing low-latency applications
  • Have worked at the intersection of distributed systems, ML, and product before
  • Have experience in multi-threading and asynchronous programming
  • Have experience in architecting large-scale distributed systems
  • Have contributed at any stage in the lifecycle of LLMs. Including but not limited to data creation, training techniques to optimise inference.

Experience: 2–5 years (or equivalent depth)

Compensation: No cap for the right talent + Real ESOPs

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