Founding ai engineer

3 weeks 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. 2 M+ 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, Deep Mind, 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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