Job Description
Company Description
Super Bryn AI develops reliability infrastructure for enterprise voice agents, focusing on enhancing the performance of voice AI systems in real-world, production settings. Our tools provide evaluation, observability, and self-learning capabilities to help enterprises improve their voice agents continuously, particularly in regulated industries where reliability is critical. Founded by experts with roots in voice AI research from IIT Madras and King's College London, Super Bryn AI is backed by Kalaari Capital.
Role Description
This is a full-time on-site role located in Bengaluru, Karnataka for a Founding AI Engineer specializing in Voice AI Infrastructure. The responsibilities include designing and implementing scalable systems to support voice AI, developing techniques for pattern recognition and natural language processing, and optimizing neural network models for real-world production scenarios. Collaborating with a focused team, you will contribute to building the foundational technologies critical to the improvement of enterprise voice agents.
To kick things off, here's a small hands-on task: Bryn-AI-Engineer-Task-2e6d df8075bb8fc1e0ae
You can submit the Git Hub repos, deployed link and resume/linkedin profile to - we care about how you think and build than where you've been!
Below details will give you a sense of what the role entails.
What you’ll work on:
- Design and build backend and/or full-stack systems for a voice-first AI platform
- Build pipelines for conversation data: transcripts, audio metadata, embeddings, and
- derived metrics
- Develop APIs for real-time and offline analysis of conversational systems
- Help design and implement evaluation workflows for voice agents and LLM-based
- systems
- Build reliable systems for logging, tracing, and metrics
- Participate in system design discussions around scalability, latency, and failure
- modes
- Deploy, operate, and iterate on services running on AWS / GCP / Azure
- Work closely with the founders on product and technical direction
- You’ll own features end-to-end - from design to production.
What we’re looking for:
We’re looking for engineers who enjoy building real systems and owning outcomes.
You should have:
- 1.5-3 years of hands-on software engineering experience
- Strong backend or full-stack fundamentals
- Prior experience with conversational AI systems (voice agents are a strong plus)
- Comfort working with LLMs, agent workflows, or dialogue systems
- Familiarity with databases (e.g., Postgre SQL, time-series DBs, or similar)
- Experience designing and consuming APIs
- Working knowledge of system design and backend architecture
- Experience deploying and running software on cloud infrastructure
- Ability to move fast, prototype ideas, and continuously improve them
- We value curiosity, ownership, and good engineering judgment over perfect resumes.
Bonus points
- Exposure to evaluation frameworks, monitoring, or observability systems (voice-, conversation-, or LLM-based)
- Experience with speech-to-text or text-to-speech pipelines
- Worked with audio data, streaming systems, or latency-sensitive workloads
- Familiarity with metrics, logging, and tracing tools
- Experience with embeddings, vector databases, or retrieval systems
- Prior startup or early-stage company experience
NOTE: We only evaluate profiles of folks who have submitted the task. If there are any doubts about the task, feel free to write in to
*************
Super Bryn AI develops reliability infrastructure for enterprise voice agents, focusing on enhancing the performance of voice AI systems in real-world, production settings. Our tools provide evaluation, observability, and self-learning capabilities to help enterprises improve their voice agents continuously, particularly in regulated industries where reliability is critical. Founded by experts with roots in voice AI research from IIT Madras and King's College London, Super Bryn AI is backed by Kalaari Capital.
Role Description
This is a full-time on-site role located in Bengaluru, Karnataka for a Founding AI Engineer specializing in Voice AI Infrastructure. The responsibilities include designing and implementing scalable systems to support voice AI, developing techniques for pattern recognition and natural language processing, and optimizing neural network models for real-world production scenarios. Collaborating with a focused team, you will contribute to building the foundational technologies critical to the improvement of enterprise voice agents.
To kick things off, here's a small hands-on task: Bryn-AI-Engineer-Task-2e6d df8075bb8fc1e0ae
You can submit the Git Hub repos, deployed link and resume/linkedin profile to - we care about how you think and build than where you've been!
Below details will give you a sense of what the role entails.
What you’ll work on:
- Design and build backend and/or full-stack systems for a voice-first AI platform
- Build pipelines for conversation data: transcripts, audio metadata, embeddings, and
- derived metrics
- Develop APIs for real-time and offline analysis of conversational systems
- Help design and implement evaluation workflows for voice agents and LLM-based
- systems
- Build reliable systems for logging, tracing, and metrics
- Participate in system design discussions around scalability, latency, and failure
- modes
- Deploy, operate, and iterate on services running on AWS / GCP / Azure
- Work closely with the founders on product and technical direction
- You’ll own features end-to-end - from design to production.
What we’re looking for:
We’re looking for engineers who enjoy building real systems and owning outcomes.
You should have:
- 1.5-3 years of hands-on software engineering experience
- Strong backend or full-stack fundamentals
- Prior experience with conversational AI systems (voice agents are a strong plus)
- Comfort working with LLMs, agent workflows, or dialogue systems
- Familiarity with databases (e.g., Postgre SQL, time-series DBs, or similar)
- Experience designing and consuming APIs
- Working knowledge of system design and backend architecture
- Experience deploying and running software on cloud infrastructure
- Ability to move fast, prototype ideas, and continuously improve them
- We value curiosity, ownership, and good engineering judgment over perfect resumes.
Bonus points
- Exposure to evaluation frameworks, monitoring, or observability systems (voice-, conversation-, or LLM-based)
- Experience with speech-to-text or text-to-speech pipelines
- Worked with audio data, streaming systems, or latency-sensitive workloads
- Familiarity with metrics, logging, and tracing tools
- Experience with embeddings, vector databases, or retrieval systems
- Prior startup or early-stage company experience
NOTE: We only evaluate profiles of folks who have submitted the task. If there are any doubts about the task, feel free to write in to
*************
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