Lead Machine Learning Engineer - NLP
- Trigo Quality
- 10 to 14 Yrs
- Delhi, NCR
- 8 months ago
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Job Description
- You are a highly experienced Lead Machine Learning Engineer specializing in Speech AI, Natural Language Processing (NLP), and Generative AI (GenAI). Your role is crucial in designing and expanding a production-grade speech-based virtual assistant powered by Large Language Models (LLMs), advanced audio signal processing, and multimodal intelligence. Collaborating closely with product, research, and DevOps teams, you will lead a group of ML engineers to create and implement cutting-edge AI solutions. In this role, your responsibilities include architecting and implementing advanced machine learning models for speech recognition (ASR), text-to-speech (TTS), NLP, and multimodal tasks. You will lead the development and fine-tuning of Transformer-based LLMs, including encoder-decoder architectures for audio and text tasks. Additionally, you will build custom audio-LLM interaction frameworks incorporating modality fusion, speech understanding, and language generation techniques. Your duties also involve designing and deploying LLM-powered virtual assistants with real-time speech interfaces for dialog, voice commands, and assistive technologies. You will integrate speech models with backend NLP pipelines to handle complex user intents, contextual understanding, and response generation effectively. Furthermore, you will design and implement end-to-end ML pipelines covering data ingestion, preprocessing, feature extraction, model training, evaluation, and deployment. Developing reproducible and scalable training pipelines using MLOps tools such as MLflow, Kubeflow, and Airflow with robust monitoring and model versioning will be part of your responsibilities. You will also drive CI/CD for ML workflows, model containerization (Docker), and orchestration using Kubernetes/serverless infrastructure. To stay updated with the latest advancements in Speech AI, LLMs, and GenAI, you will evaluate and drive the adoption of novel techniques. Experimenting with self-supervised learning, prompt tuning, parameter-efficient fine-tuning (PEFT), and zero-shot/multilingual speech models will be essential for innovation and progress. The required technical skills for this role include: - 10+ years of hands-on experience in machine learning, with a deep focus on audio (speech) and NLP applications. - Expertise in Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) systems, including tools like Wav2Vec, Whisper, Tacotron, FastSpeech, etc. - Strong knowledge of Transformer architectures like BERT, GPT, T5, and encoder-decoder LLM variants, including training/fine-tuning at scale. - Proficiency in Python programming and deep learning frameworks like PyTorch and TensorFlow. - In-depth understanding of audio signal processing concepts such as MFCCs, spectrograms, wavelets, sampling, filtering, etc. - Experience with multimodal machine learning, including the fusion of speech, text, and contextual signals. - Deployment of ML services with Docker, Kubernetes, and experience with distributed training setups on GPU clusters or cloud platforms (AWS, GCP, Azure). - Proven experience in building production-grade MLOps frameworks and maintaining model lifecycle management. - Experience with real-time inference, latency optimization, and efficient decoding techniques for audio/NLP systems. Preferred qualifications for this role include: - Master's or Ph.D. in Computer Science, Machine Learning, Signal Processing, or related technical discipline. - Publications or open-source contributions in speech, NLP, or GenAI. - Familiarity with LLM alignment techniques, RLHF, prompt engineering, and fine-tuning using LoRA, QLoRA, or adapters. - Previous experience in deploying voice-based conversational AI products at scale.,
Employement Category:
Employement Type: Full timeIndustry: IT Services & ConsultingRole Category: Not SpecifiedFunctional Area: Not SpecifiedRole/Responsibilies: Lead Machine Learning Engineer - NLPContact Details:
Company: Ekloud Inc.Location(s): Delhi, NCR
Keyskills: Machine Learning Airflow Docker Kubernetes Python Wavelets Sampling PyTorch TensorFlow
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