Founding AI Engineer

11 hours ago

Bangalore, 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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