LLM Engineer

About the Role

As a Senior LLM Engineer
, you will be responsible for the end-to-end development, optimization, and deployment of large language models. You'll work on challenging problems at the intersection of deep learning, natural language processing, and distributed computing.

• Analyze large and complex datasets to extract meaningful insights and inform data-driven decision-making.
• Develop, train, and deploy predictive models to enhance the capabilities of our AI solutions.
• Collaborate with cross-functional teams to understand business objectives and translate them into actionable data science tasks.
• Design and implement advanced LLM architectures, including transformer-based models and their variants.
• Develop novel attention mechanisms and positional encoding schemes.
• Experiment with model scaling techniques and efficient architectures (e.g., MoE, sparse transformers).
• Continuously evaluate and improve existing models based on real-world performance and evolving business needs.
• Implement and optimize distributed training pipelines for large-scale models.
• Develop strategies for efficient fine-tuning, including parameter-efficient techniques (e.g., LoRA, prefix tuning).
• Apply advanced optimization techniques such as mixed-precision training and gradient accumulation.
• Optimize models for inference, including quantization and pruning techniques.
• Implement efficient serving solutions for real-time inference.
• Develop strategies for model compression and knowledge distillation.
• Develop task-specific algorithms for applications such as text classification, named entity recognition, and question-answering.
• Work with MLOps teams to design and maintain training and serving infrastructure.

• 5+ years of experience in deep learning and NLP, with a focus on large language models.
• Master's or Ph.D. in Data Science, Statistics, Computer Science, or a related field.
• Expert-level proficiency in Python and at least one deep learning framework (
  Py Torch ,
  Tensor Flow
  , or JAX
  ).
• Strong understanding of transformer architectures
  , attention mechanisms
  , and recent advancements in LLMs.
• Experience with distributed training frameworks (e.g.,
  Deep Speed
  , Megatron-LM
  ).
• Proficiency in optimizing model performance using techniques like mixed-precision training
  , gradient checkpointing
  , and model parallelism
  .
• Understanding of NLP algorithms such as tokenization
  , parsing
  , and semantic analysis
  .
• Experience with sequence-to-sequence models and self-supervised learning techniques
  .
• Experience with both SQL and No
  
  SQL databases for managing training data and model artifacts.