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AI Engineering Lead
2 weeks ago
Vsynergize AI is seeking a highly skilled Senior Large Language Model Engineer to join our team. As a key member of our AI engineering team, you will be responsible for designing, developing, and optimizing AI-powered conversational systems that enhance customer engagement and support automation.
This role involves experimenting at scale with different large language models (LLMs) and LLM frameworks like Langchain to build and deploy scalable chatbot and customer support applications. You will also work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders, to create state-of-the-art AI-powered solutions.
The ideal candidate will have strong background in LLMs, NLP, and deep learning frameworks such as TensorFlow, PyTorch, or Hugging Face. Experience deploying applications in cloud environments (AWS and Azure) is also essential.
Key Responsibilities
* Design and develop AI-powered conversational systems using LLMs and LLM frameworks
* Experiment at scale with different LLMs and LLM frameworks to build and deploy scalable chatbot and customer support applications
* Develop and optimize chatbots using Rasa, Amazon Lex, Microsoft Bot Framework, Dialogflow, etc.
* Implement AI-driven enhancements in contact centers, including AI-powered IVRs, voice bots, and agent assist tools
* Build scalable, reliable, and efficient AI-based solutions for high-volume customer interactions
* Prepare and process structured and unstructured datasets for evaluation of LLMs and optimize performance of conversational AI systems
Required Qualifications
* 5+ years of experience in AI/ML, NLP, or chatbot development
* Strong background in LLMs, NLP, and deep learning frameworks such as TensorFlow, PyTorch, or Hugging Face
* Experience deploying applications in cloud environments (AWS and Azure)
* Proficiency in Python, JavaScript, or related programming languages
* Hands-on experience with speech-to-text, text-to-speech, and voice AI technologies
* Familiarity with MLOps practices, model monitoring, and continuous learning frameworks