Multimodal AI Model Developer

1 week ago

Guntur, Andhra Pradesh, India beBeeNlp Full time ₹ 10,00,000 - ₹ 20,00,000
Research and Development

FlashIntel seeks a dedicated and innovative Research Engineer with expertise in Multimodal Large Language Models (m-LLMs), Text-to-Speech (TTS) technologies, and agentic workflows. This position offers a unique opportunity to engage in cutting-edge research and development of AI solutions that integrate various data modalities and enhance interactive systems.

Key Responsibilities:

  1. Model Design & Optimization: Participate in developing, fine-tuning, and evaluating AI models for complex NLP tasks, with a focus on TTS and multimodal integration.
  2. Optimize models for scalability, efficiency, and deployment in real-world production systems, ensuring seamless interaction between components in agentic workflows.
  3. Experiment with novel methods in model training, domain adaptation, and performance evaluation to enhance the naturalness and responsiveness of TTS systems.

Collaboration & Innovation:

  1. Work closely with cross-functional teams, including product, engineering, and data science, to translate research insights into tangible products that utilize m-LLMs and TTS technologies within agentic workflows.
  2. Engage in a culture of learning and innovation, contributing to team knowledge sharing on m-LLMs, TTS, and AI agents.
  3. Collaborate with external research communities, potentially contributing to conferences and publications in the fields of m-LLMs, TTS, and agentic AI systems.

Required Qualifications:

  1. Educational Background: Currently pursuing an advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, NLP, or a closely related field.
  2. Technical Expertise: Experience or coursework in large language models, deep learning, NLP, and TTS technologies.
  3. Proficiency in programming languages such as Python, with experience in frameworks like TensorFlow or PyTorch.
  4. Understanding of algorithm design, data structures, and model optimization techniques relevant to m-LLMs and TTS systems.

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