Machine Learning Engineer

As a Machine Learning Engineer at Nurix AI, you will play a pivotal role in solving some of the hardest challenges in conversational AI. You'll work on building robust ASR/TTS systems for multilingual Indian contexts, designing guardrails that go beyond IVR-like flows, and ensuring real-time, low-latency conversational agents that can handle human pauses and interruptions. This role is ideal for engineers who want to push the boundaries of speech, LLMs, and agentic AI in production at scale.

The core responsibilities for the job include the following:

Speech and Voice Systems:

• Design, train, and optimize custom ASR/TTS models to overcome limitations of Sarvam ASR and improve accuracy across Indian multilingual settings.
• Build conversational agents that can handle interruptions, natural pauses, and real-world voice variability without breakdowns.

Core ML/AI Development:

• Contribute to Voice-to-Voice (V2V) systems using state space models
• Develop agentic RAG pipelines and LLM orchestration for intelligent, context-aware voice agents.
• Work on self-learning agents capable of continuous improvement by observing human workflows and feedback

Engineering and Deployment:

• Collaborate with MLOps to bring models into real-time, low-latency production environments.
• Ensure scalability, security, and reliability of deployed ML systems.

Collaboration and Research:

• Partner closely with product and engineering teams to deliver production-ready features.
• Stay on top of the latest advances in ASR, TTS, NLP, LLMs, and real-time inference frameworks and apply them to Nurix's roadmap.

Requirements:

• Bachelor's degree in computer science, AI/ML, or a related field.
• 4+ years of hands-on ML engineering experience, with a focus on speech/NLP systems.
• Strong expertise in ASR, TTS, NLP, LLM fine-tuning, or dialogue systems.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
• Experience in designing low-latency, real-time ML applications.
• Strong understanding of the ML lifecycle, evaluation, and deployment practices.
• Contributions to open-source ML projects or research publications.
• Prior work on multilingual or Indian-context ASR/TTS systems.