▷ 15h Left: Founding AI Engineer – AI Infra

2 weeks 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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