Senior LLM Engineer

Roles and Responsibilities:

• Model Expertise

  : Work with transformer models such as

  GPT

  ,

  BERT

  ,

  T5

  ,

  RoBERTa

  , and others for a variety of NLP tasks, including text generation, summarization, classification, and translation.

• Model Fine-Tuning

  : Fine-tune pre-trained models on

  domain-specific datasets

  to improve performance for specific applications such as summarization, text generation, and question answering.

• Prompt Engineering

  : Craft clear, concise, and contextually relevant

  prompts

  to guide transformer-based models towards generating desired outputs for specific tasks. Iterate on prompts to optimize model performance.

• Instruction-Based Prompting

  : Implement

  instruction-based prompting

  to guide the model toward achieving specific goals, ensuring that the outputs are contextually accurate and aligned with task objectives.

• Zero-shot, Few-shot, Many-shot Learning

  : Utilize

  zero-shot

  ,

  few-shot

  , and

  many-shot learning

  techniques to improve model performance without the need for full retraining.

• Chain-of-Thought (CoT) Prompting

  : Implement

  Chain-of-Thought (CoT) prompting

  to guide models through complex reasoning tasks, ensuring that the outputs are logically structured and provide step-by-step explanations.

• Model Evaluation

  : Use

  evaluation metrics

  such as

  BLEU

  ,

  ROUGE

  , and other relevant metrics to assess and improve the performance of models for various NLP tasks.

• Model Deployment

  : Support the

  deployment

  of trained models into production environments and integrate them into existing systems for real-time applications.

• Bias Awareness

  : Be aware of and mitigate issues related to

  bias

  ,

  hallucinations

  , and

  knowledge cutoffs

  in LLMs, ensuring high-quality and reliable outputs.

• Collaboration

  : Collaborate with cross-functional teams including engineers, data scientists, and product managers to deliver efficient and scalable NLP solutions.

Must Have Skill

• Overall 7 years with at least

  5+ years

  of experience working with

  transformer-based models

  and

  NLP tasks

  , with a focus on

  text generation

  ,

  summarization

  ,

  question answering

  ,

  classification

  , and similar tasks.
• Expertise in

  transformer models

  like

  GPT (Generative Pre-trained Transformer)

  ,

  BERT (Bidirectional Encoder Representations from Transformers)

  ,

  T5 (Text-to-Text Transfer Transformer)

  ,

  RoBERTa

  , and similar models.
• Familiarity with model architectures, attention mechanisms, and

  self-attention layers

  that enable LLMs to generate human-like text.
• Experience in

  fine-tuning pre-trained models

  on domain-specific datasets for tasks such as

  text generation

  ,

  summarization

  ,

  question answering

  ,

  classification

  , and

  translation

  .
• Familiarity with concepts like

  attention mechanisms

  ,

  context windows

  ,

  tokenization

  , and

  embedding layers

  .
• Awareness of

  biases

  ,

  hallucinations

  , and

  knowledge cutoffs

  that can affect LLM performance and output quality.
• Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs.
• Experience in

  instruction-based prompting

• Use of

  zero-shot

  ,

  few-shot

  , and

  many-shot

  learning techniques for maximizing model performance without retraining.
• Experience in

  iterating

  on prompts to refine outputs, test model performance, and ensure consistent results.
• Crafting

  prompt templates

  for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs.
• Expertise in

  chain-of-thought (CoT)

  prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns.
• Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK).
• Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks.
• Experience in training, fine-tuning, and deploying machine learning models in an NLP context.
• Understanding of model evaluation metrics (e.g., BLEU, ROUGE)

Qualification:

• BE/B.Tech or Equivalent degree in Computer Science or related field.
• Excellent communication skills in English, both verbal and written