Bangalore, Karnataka, India
Information Technology
Full-Time
Ekloud, Inc.
Overview
About The Role
We are seeking a highly experienced and technically deep Lead Machine Learning Engineer with a specialization in Speech AI, Natural Language Processing (NLP), and Generative AI (GenAI). This role is instrumental in architecting and scaling a production-grade speech-based virtual assistant powered by Large Language Models (LLMs), advanced audio signal processing, and multimodal intelligence. You will lead a team of ML engineers and collaborate closely with product, research, and DevOps stakeholders to develop and deploy cutting-edge AI solutions.
Key Responsibilities
ML Architecture & Modeling :
We are seeking a highly experienced and technically deep Lead Machine Learning Engineer with a specialization in Speech AI, Natural Language Processing (NLP), and Generative AI (GenAI). This role is instrumental in architecting and scaling a production-grade speech-based virtual assistant powered by Large Language Models (LLMs), advanced audio signal processing, and multimodal intelligence. You will lead a team of ML engineers and collaborate closely with product, research, and DevOps stakeholders to develop and deploy cutting-edge AI solutions.
Key Responsibilities
ML Architecture & Modeling :
- Architect, design, and implement advanced machine learning models across speech recognition (ASR), text-to-speech (TTS), NLP, and multimodal tasks.
- Lead the development and fine-tuning of Transformer-based LLMs, including encoder-decoder architectures for audio and text tasks.
- Build custom audio-LLM interaction frameworks, including techniques like modality fusion, speech understanding, and language generation.
- Design and deploy LLM-powered virtual assistants with real-time speech interfaces for dialog, voice commands, and assistive technologies.
- Integrate speech models with backend NLP pipelines to handle complex user intents, contextual understanding, and response generation.
- Design and implement end-to-end ML pipelines covering data ingestion, preprocessing, feature extraction, model training, evaluation, and deployment.
- Develop reproducible and scalable training pipelines using MLOps tools (e.g., MLflow, Kubeflow, Airflow) with robust monitoring and model versioning.
- Drive CI/CD for ML workflows, containerization of models (Docker), and orchestration using Kubernetes/serverless infrastructure.
- Stay up to date with state-of-the-art publications in Speech AI, LLMs, and GenAI; evaluate applicability and drive adoption of novel techniques.
- Experiment with cutting-edge self-supervised learning, prompt tuning, parameter-efficient fine-tuning (PEFT), and zero-shot/multilingual speech models.
- 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, such as BERT, GPT, T5, and encoder-decoder LLM variants, including training/fine-tuning at scale.
- Solid programming expertise in Python, with proficiency in deep learning frameworks like PyTorch and TensorFlow.
- In-depth understanding of audio signal processing concepts : MFCCs, spectrograms, wavelets, sampling, filtering, etc.
- Experience with multimodal machine learning, including fusion of speech, text, and contextual signals.
- Proficient in deploying ML services with Docker, Kubernetes, and experience with distributed training setups on GPU clusters or cloud (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.
- 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.
- Prior experience deploying voice-based conversational AI products at scale.
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