Machine Learning Engineer

As a Machine Learning Engineer, you will play a pivotal role in solving some of the hardest challenges. You'll work on building robust agentic systems, designing evals and managing model deployment at scale. 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:

• Own evaluation, selection, and integration of ASR/LLM/TTS/S2S model providers. Benchmark accuracy, latency, and noise robustness across language and code-switched settings, including noise cancellation and audio pre-processing variants.
• Build and optimise real-time streaming voice pipelines that handle interruptions, endpointing, natural pauses, and real-world audio variability without breakdowns.

Model Quality and Inference:

• Analyse the conversational quality of voice agents against human benchmarks and translate findings into prioritised model improvement roadmaps (fine-tuning, prompting, and harness engineering).
• Design SFT datasets and lightweight eval frameworks for voice-agent quality at scale.
• Optimise LLM/ASR/TTS inference for in-house model deployments, including memory/KV-cache planning, GPU selection and sizing, and latency-throughput trade-offs for real-time voice workloads.
• Harden prompt, harness and tool-calling reliability for production agents.
• Work on self-learning agents capable of continuous improvement by observing human workflows and feedback

Engineering and Deployment:

• Collaborate with MLOps and inference providers to bring models into real-time, low-latency production environments.
• Work with DataOps to create, refine and build high-quality real-world datasets for in-house data needs.
• Ensure scalability, security, and reliability of deployed ML systems.

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 inference and fine-tuning, and conversational voice systems.
• Track record of training/finetuning models and deploying them in production.
• Worked closely with data operation teams for training/testing data annotation and post-release sanity checks.
• Proficiency in Python backends and ML frameworks (PyTorch, vLLM, SGLang, Unsloth, and Axolotl).
• Experience in designing low-latency, real-time ML applications.
• Strong understanding of 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.