Lead Assistant Manager

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About Us

Job Responsibilities

• Develope initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents.
• Design and implement advanced workflows for integrating LLMs into real-world applications across various domains such as Finance, Insurance, and Healthcare.
• Develop and fine-tune domain-specific LLMs to optimize performance, using techniques like prompt engineering, adapter-based tuning, or low-rank adaptation.
• Drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques.
• Stay at the forefront of AI advancements by reading, adapting, and implementing cutting-edge research to solve real-world challenges.
• Document research findings, methodologies, and implementations for internal and external stakeholders.
  

Qualifications

Experience:

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Required Skills

• Core Expertise:
• Proven experience with Large Language Models (e.g., GPT-4, BERT, LLaMA, PaLM) and fine-tuning them for domain-specific applications.
• In-depth knowledge of Retrieval-Augmented Generation workflows, including retrieval system design and document store integration.
• Hands-on experience developing and deploying autonomous agents for complex problem-solving tasks.
• Tools & Frameworks:
• Proficiency in deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.
• Experience with distributed training and optimization on GPUs and TPUs.
• Familiarity with cloud ecosystems (AWS, Azure, Google Cloud) practices for scalable deployment.
• Research & Development:
• Ability to read and adapt cutting-edge research papers for applied solutions in LLMs and knowledge graphs.
• Expertise in domain adaptation, few-shot learning, and zero-shot reasoning.
• Strong understanding of generative models, including VAEs, GANs, or diffusion models, and their integration with LLMs.
• Problem Solving:
• Demonstrated ability to address challenges in unstructured data processing, including NLP and multimodal scenarios.
• Experience with document retrieval, clustering, and unsupervised learning techniques.
  

Preferred Skills

• Experience with LLM fine-tuning and building Agentic systems for domain LLMs.
• Experience with reinforcement learning and fine-tuning via RLHF (Reinforcement Learning with Human Feedback).
• Knowledge of large-scale optimization methods for model training and inference.
• Familiarity with knowledge distillation and efficient model compression techniques.
• Strong collaboration and communication skills, with a proven ability to lead teams.
• Experience with MoE based architecture and knowledge on federated learning.