Data Scientist

Job Title:

Job Summary

Data Scientist with deep expertise in NLP and Generative AI

Key Responsibilities

• Fine-tune and evaluate LLMs (e.g., Mistral, LLaMA, Qwen) using frameworks like

  Unsloth

  , HuggingFace, and DeepSpeed
• Develop high-quality

  prompts and RAG pipelines

  for few-shot and zero-shot performance
• Analyze and curate domain-specific text datasets for training and evaluation
• Conduct performance and safety evaluation of fine-tuned models
• Collaborate with engineering teams to integrate models into

  agentic workflows

• Stay up to date with the latest in open-source LLMs and GenAI tools, and rapidly prototype experiments
• Apply efficient training and inference techniques (LoRA, QLoRA, quantization, etc.)

Required Skills

• 3+ years of experience in

  Natural Language Processing (NLP)

  and

  machine learning

  applied to text
• Strong coding skills in python
• Hands-on experience fine-tuning

  LLMs

  (e.g., LLaMA, Mistral, Falcon, Qwen) using frameworks like

  Unsloth

  ,

  HuggingFace Transformers

  ,

  PEFT

  ,

  LoRA

  ,

  QLoRA

  ,

  bitsandbytes

• Proficient in

  PyTorch

  (preferred) or

  TensorFlow

  , with experience in writing custom training/evaluation loops
• Experience in

  dataset preparation

  , tokenization (e.g., Tokenizer, tokenizers), and formatting for instruction tuning (ChatML, Alpaca, ShareGPT formats)
• Familiarity with

  retrieval-augmented generation (RAG)

  using

  FAISS

  ,

  Chroma

  ,

  Weaviate

  , or

  Qdrant

• Strong knowledge of

  prompt engineering

  ,

  few-shot/zero-shot learning

  ,

  chain-of-thought prompting

  , and

  function-calling patterns

• Exposure to

  agentic AI frameworks

  like

  CrewAI

  ,

  Phidata

  ,

  LangChain

  ,

  LlamaIndex

  , or

  AutoGen

• Experience with

  GPU-accelerated training/inference

  and libraries like

  DeepSpeed

  ,

  Accelerate

  ,

  Flash Attention

  ,

  Transformers v2

  , etc.
• Solid understanding of

  LLM evaluation metrics

  (BLEU, ROUGE, perplexity, pass@k) and safety-related metrics (toxicity, bias)
• Ability to work with open-source checkpoints and formats (e.g.,

  safetensors

  ,

  GGUF

  ,

  HF Hub

  ,

  GPTQ

  ,

  ExLlama

  )
• Comfortable with

  containerized environments

  (Docker) and scripting for

  training pipelines

  ,

  data curation

  , or

  evaluation workflows

Nice to Haves

• Experience in Linux (Ubuntu)
• Terminal/Bash Scripting