Data Scientist

Job Summary

We are looking for a hands-on Data Scientist with deep expertise in NLP and Generative AI to help build and refine the intelligence behind our agentic AI systems. You will be responsible for fine-tuning, prompt engineering, and evaluating LLMs that power our digital workers across real-world workflows.

Key Responsibilities

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  Fine-tune and evaluate LLMs (e.g., Mistral, LLaMA, Qwen) using frameworks like Unsloth, HuggingFace, and DeepSpeed
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  Develop high-quality prompts and RAG pipelines for few-shot and zero-shot performance
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  Analyze and curate domain-specific text datasets for training and evaluation
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  Conduct performance and safety evaluation of fine-tuned models
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  Collaborate with engineering teams to integrate models into agentic workflows
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  Stay up to date with the latest in open-source LLMs and GenAI tools, and rapidly prototype experiments
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  Apply efficient training and inference techniques (LoRA, QLoRA, quantization, etc.)

Requirements

• 3+ years of experience in Natural Language Processing (NLP) and machine learning applied to text
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  Strong coding skills in python
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  Hands-on experience fine-tuning LLMs (e.g., LLaMA, Mistral, Falcon, Qwen) using frameworks like Unsloth, HuggingFace Transformers, PEFT, LoRA, QLoRA, bitsandbytes
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  Proficient in PyTorch (preferred) or TensorFlow, with experience in writing custom training/evaluation loops
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  Experience in dataset preparation, tokenization (e.g., Tokenizer, tokenizers), and formatting for instruction tuning (ChatML, Alpaca, ShareGPT formats)
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  Familiarity with retrieval-augmented generation (RAG) using FAISS, Chroma, Weaviate, or Qdrant
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  Strong knowledge of prompt engineering, few-shot/zero-shot learning, chain-of-thought prompting, and function-calling patterns
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  Exposure to agentic AI frameworks like CrewAI, Phidata, LangChain, LlamaIndex, or AutoGen
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  Experience with GPU-accelerated training/inference and libraries like DeepSpeed, Accelerate, Flash Attention, Transformers v2, etc.
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  Solid understanding of LLM evaluation metrics (BLEU, ROUGE, perplexity, pass@k) and safety-related metrics (toxicity, bias)
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  Ability to work with open-source checkpoints and formats (e.g., safetensors, GGUF, HF Hub, GPTQ, ExLlama)
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  Comfortable with containerized environments (Docker) and scripting for training pipelines, data curation, or evaluation workflows

Nice to Haves

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  Experience in Linux (Ubuntu)
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  Terminal/Bash Scripting