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