Senior Machine Learning Engineer

Senior Machine Learning Engineer – NLP, Speech & LLMs Location: Coimbatore

We are looking for a Senior Machine Learning Engineer to design, build, and deploy core ML models powering an AI-driven SaaS experience platform. This role is ideal for someone who enjoys hands-on model development, working on NLP, Speech-to-Text (STT), Text-to-Speech (TTS), Speech Language Models, and LLM-based systems, and taking models from research to production. You will work closely with researchers, product teams, and platform engineers to deliver scalable, production-grade ML systems.

Key Responsibilities Design, train, fine-tune, and evaluate ML models for NLP and speech use cases Build and optimize STT, TTS, and speech-language models Develop and experiment with transformer-based and deep learning architectures Prepare and manage large-scale text and speech datasets (cleaning, augmentation, labeling) Implement training pipelines, evaluation metrics, and benchmarking frameworks Optimize models for performance, latency, and cost in production Collaborate with engineering teams to deploy models as scalable services Monitor model performance and handle retraining and improvements Stay up to date with the latest research and apply it to real-world problems Mentor junior ML engineers and contribute to best practices

Required Skills & Qualifications 5–8+ years of experience in Machine Learning / Applied AI Strong hands-on experience in NLP and Speech Processing Proven experience building and training ML models (not just API usage) Solid experience with STT, TTS, and speech language models Strong understanding of deep learning fundamentals and transformer architectures Proficiency in Python and ML frameworks such as PyTorch (preferred) or TensorFlow Experience with libraries and toolkits such as Hugging Face, ESPnet, Fairseq, Kaldi, NeMo, Whisper (fine-tuning) Experience with LLMs, including fine-tuning, adapters (LoRA), and RAG pipelines Familiarity with GPU-based training, distributed training, and optimization techniques Strong grounding in statistics, linear algebra, and optimization

Preferred / Nice-to-Have Experience with self-supervised learning for speech or language (e.g., wav2vec, HuBERT) Knowledge of real-time or low-latency inference systems Experience with model compression, quantization, and distillation Exposure to MLOps tools (MLflow, Weights & Biases, Kubeflow, Airflow) Experience with cloud platforms (AWS, GCP, Azure) Open-source contributions or research publications